
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 16:34:07 2026
Logical CPU cores: 128
/home/robbe/bin/diann-2.5.0/diann-linux --f /public/local/ProteoBench/HYE_Astral/LFQ_Astral_DIA_15min_50ng_Condition_A_REP1.raw --f /public/local/ProteoBench/HYE_Astral/LFQ_Astral_DIA_15min_50ng_Condition_A_REP2.raw --f /public/local/ProteoBench/HYE_Astral/LFQ_Astral_DIA_15min_50ng_Condition_A_REP3.raw --f /public/local/ProteoBench/HYE_Astral/LFQ_Astral_DIA_15min_50ng_Condition_B_REP1.raw --f /public/local/ProteoBench/HYE_Astral/LFQ_Astral_DIA_15min_50ng_Condition_B_REP2.raw --f /public/local/ProteoBench/HYE_Astral/LFQ_Astral_DIA_15min_50ng_Condition_B_REP3.raw --fasta /public/local/ProteoBench/fastas/ProteoBenchFASTA_MixedSpecies_HYE.fasta --out /home/robbe/PB_output/results/test_run/HYE_Astral/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
[0:07] Processing FASTA
[0:10] Assembling elution groups
[0:19] 8103720 precursors generated
[0:19] Protein names missing for some isoforms
[0:19] Gene names missing for some isoforms
[0:19] Library contains 31680 proteins, and 0 genes
WARNING: no gene information in the FASTA or library: consider using --ids-to-names
[0:25] [0:42] [8:29] [9:26] [9:29] [9:36] Saving the library to /home/robbe/PB_output/results/test_run/HYE_Astral/diann_v2.5.0/report-lib.predicted.speclib
[9:43] Initialising library
[10:03] Loading spectral library /home/robbe/PB_output/results/test_run/HYE_Astral/diann_v2.5.0/report-lib.predicted.speclib
[10:06] Library annotated with sequence database(s): /public/local/ProteoBench/fastas/ProteoBenchFASTA_MixedSpecies_HYE.fasta
[10:08] Spectral library loaded: 31832 protein isoforms, 43199 protein groups and 8103720 precursors in 3825450 elution groups (targets and decoys).
[10:08] Loading protein annotations from FASTA /public/local/ProteoBench/fastas/ProteoBenchFASTA_MixedSpecies_HYE.fasta
[10:08] Annotating library proteins with information from the FASTA database
[10:08] Protein names missing for some isoforms
[10:08] Gene names missing for some isoforms
[10:08] Library contains 31680 proteins, and 0 genes
WARNING: no gene information in the FASTA or library: consider using --ids-to-names
[10:15] Initialising library
WARNING: it is strongly recommended to enable MBR when analysing with a large library, if this is a quantitative analysis

[10:30] File #1/6
[10:30] Loading run /public/local/ProteoBench/HYE_Astral/LFQ_Astral_DIA_15min_50ng_Condition_A_REP1.raw
[11:41] Pre-processing...
[11:44] 2931 MS1 and 293271 MS2 scans in 977 (inferred) and 977 (encoded) cycles, 7009928 precursors in range
[11:45] Calibrating with mass accuracies 21 (MS1), 25 (MS2)
[12:07] RT window set to 1.21828
[12:07] Peak width: 2.94
[12:07] Scan window radius set to 6
[12:07] Recommended MS1 mass accuracy setting: 2.6 ppm
[12:19] Searching decoys
[13:29] Main search
[15:45] Removing low confidence identifications
[16:20] Removing interfering precursors
[16:41] Training neural networks on 216917 target and 189866 decoy PSMs
[17:37] Training neural networks on 216917 target and 185805 decoy PSMs
[18:29] Precursors at 1% peptidoform FDR: 96005
[18:31] Number of IDs at 0.01 FDR: 97767
[18:31] Calculating protein q-values
[18:32] Number of genes identified at 1% FDR: 0 (precursor-level), 0 (protein-level) (inference performed using proteotypic peptides only)
[18:32] Quantification
[18:34] Precursors with scored PTMs at 1% FDR: 2229 out of 2353 considered
[18:34] Precursors with all scored PTM sites unoccupied at 1% FDR: 93776
[18:34] Precursors with PTMs localised (when required) with > 90% confidence: 2142 out of 2229
[18:35] Quantification information saved to /public/local/ProteoBench/HYE_Astral/LFQ_Astral_DIA_15min_50ng_Condition_A_REP1.raw.quant

[18:35] File #2/6
[18:35] Loading run /public/local/ProteoBench/HYE_Astral/LFQ_Astral_DIA_15min_50ng_Condition_A_REP2.raw
[19:47] Pre-processing...
[19:51] 2933 MS1 and 293433 MS2 scans in 978 (inferred) and 978 (encoded) cycles, 7009928 precursors in range
[19:51] Calibrating with mass accuracies 21 (MS1), 25 (MS2)
[20:13] RT window set to 1.18611
[20:13] Recommended MS1 mass accuracy setting: 2.7 ppm
[20:24] Searching decoys
[21:31] Main search
[23:37] Removing low confidence identifications
[24:04] Removing interfering precursors
[24:23] Training neural networks on 208134 target and 179642 decoy PSMs
[25:21] Training neural networks on 208134 target and 175916 decoy PSMs
[26:17] Precursors at 1% peptidoform FDR: 96788
[26:18] Number of IDs at 0.01 FDR: 98909
[26:18] Calculating protein q-values
[26:19] Number of genes identified at 1% FDR: 0 (precursor-level), 0 (protein-level) (inference performed using proteotypic peptides only)
[26:19] Quantification
[26:21] Precursors with scored PTMs at 1% FDR: 2301 out of 2459 considered
[26:21] Precursors with all scored PTM sites unoccupied at 1% FDR: 94487
[26:21] Precursors with PTMs localised (when required) with > 90% confidence: 2224 out of 2301
[26:22] Quantification information saved to /public/local/ProteoBench/HYE_Astral/LFQ_Astral_DIA_15min_50ng_Condition_A_REP2.raw.quant

[26:22] File #3/6
[26:22] Loading run /public/local/ProteoBench/HYE_Astral/LFQ_Astral_DIA_15min_50ng_Condition_A_REP3.raw
[27:36] Pre-processing...
[27:39] 2932 MS1 and 293358 MS2 scans in 978 (inferred) and 978 (encoded) cycles, 7009928 precursors in range
[27:40] Calibrating with mass accuracies 21 (MS1), 25 (MS2)
[28:01] RT window set to 1.25091
[28:01] Recommended MS1 mass accuracy setting: 2.8 ppm
[28:13] Searching decoys
[29:19] Main search
[31:28] Removing low confidence identifications
[31:56] Removing interfering precursors
[32:15] Training neural networks on 219376 target and 192034 decoy PSMs
[33:17] Training neural networks on 219376 target and 187898 decoy PSMs
[34:13] Precursors at 1% peptidoform FDR: 97634
[34:15] Number of IDs at 0.01 FDR: 99802
[34:15] Calculating protein q-values
[34:16] Number of genes identified at 1% FDR: 0 (precursor-level), 0 (protein-level) (inference performed using proteotypic peptides only)
[34:16] Quantification
[34:18] Precursors with scored PTMs at 1% FDR: 2327 out of 2529 considered
[34:18] Precursors with all scored PTM sites unoccupied at 1% FDR: 95307
[34:18] Precursors with PTMs localised (when required) with > 90% confidence: 2236 out of 2327
[34:19] Quantification information saved to /public/local/ProteoBench/HYE_Astral/LFQ_Astral_DIA_15min_50ng_Condition_A_REP3.raw.quant

[34:19] File #4/6
[34:19] Loading run /public/local/ProteoBench/HYE_Astral/LFQ_Astral_DIA_15min_50ng_Condition_B_REP1.raw
[35:36] Pre-processing...
[35:40] 2933 MS1 and 293382 MS2 scans in 978 (inferred) and 978 (encoded) cycles, 7009928 precursors in range
[35:41] Calibrating with mass accuracies 21 (MS1), 25 (MS2)
[36:04] RT window set to 1.35416
[36:04] Recommended MS1 mass accuracy setting: 3 ppm
[36:18] Searching decoys
[37:37] Main search
[39:59] Removing low confidence identifications
[40:27] Removing interfering precursors
[40:44] Training neural networks on 221817 target and 196080 decoy PSMs
[41:45] Training neural networks on 221817 target and 191817 decoy PSMs
[42:37] Precursors at 1% peptidoform FDR: 97958
[42:39] Number of IDs at 0.01 FDR: 100139
[42:39] Calculating protein q-values
[42:40] Number of genes identified at 1% FDR: 0 (precursor-level), 0 (protein-level) (inference performed using proteotypic peptides only)
[42:40] Quantification
[42:42] Precursors with scored PTMs at 1% FDR: 2907 out of 3077 considered
[42:42] Precursors with all scored PTM sites unoccupied at 1% FDR: 95051
[42:42] Precursors with PTMs localised (when required) with > 90% confidence: 2785 out of 2907
[42:43] Quantification information saved to /public/local/ProteoBench/HYE_Astral/LFQ_Astral_DIA_15min_50ng_Condition_B_REP1.raw.quant

[42:43] File #5/6
[42:43] Loading run /public/local/ProteoBench/HYE_Astral/LFQ_Astral_DIA_15min_50ng_Condition_B_REP2.raw
[44:00] Pre-processing...
[44:03] 2933 MS1 and 293330 MS2 scans in 978 (inferred) and 978 (encoded) cycles, 7009928 precursors in range
[44:04] Calibrating with mass accuracies 21 (MS1), 25 (MS2)
[44:28] RT window set to 1.44596
[44:28] Recommended MS1 mass accuracy setting: 2.8 ppm
[44:43] Searching decoys
[46:04] Main search
[48:30] Removing low confidence identifications
[49:00] Removing interfering precursors
[49:20] Training neural networks on 225033 target and 198380 decoy PSMs
[50:22] Training neural networks on 225033 target and 193965 decoy PSMs
[51:19] Precursors at 1% peptidoform FDR: 98391
[51:21] Number of IDs at 0.01 FDR: 100274
[51:21] Calculating protein q-values
[51:22] Number of genes identified at 1% FDR: 0 (precursor-level), 0 (protein-level) (inference performed using proteotypic peptides only)
[51:22] Quantification
[51:23] Precursors with scored PTMs at 1% FDR: 2939 out of 3085 considered
[51:23] Precursors with all scored PTM sites unoccupied at 1% FDR: 95512
[51:23] Precursors with PTMs localised (when required) with > 90% confidence: 2816 out of 2939
[51:24] Quantification information saved to /public/local/ProteoBench/HYE_Astral/LFQ_Astral_DIA_15min_50ng_Condition_B_REP2.raw.quant

[51:24] File #6/6
[51:24] Loading run /public/local/ProteoBench/HYE_Astral/LFQ_Astral_DIA_15min_50ng_Condition_B_REP3.raw
[52:40] Pre-processing...
[52:45] 2934 MS1 and 293446 MS2 scans in 978 (inferred) and 978 (encoded) cycles, 7009928 precursors in range
[52:46] Calibrating with mass accuracies 21 (MS1), 25 (MS2)
[53:12] RT window set to 1.29972
[53:12] Recommended MS1 mass accuracy setting: 2.7 ppm
[53:26] Searching decoys
[54:39] Main search
[56:59] Removing low confidence identifications
[57:30] Removing interfering precursors
[57:50] Training neural networks on 220314 target and 194821 decoy PSMs
[58:50] Training neural networks on 220314 target and 191020 decoy PSMs
[59:42] Precursors at 1% peptidoform FDR: 97548
[59:44] Number of IDs at 0.01 FDR: 99969
[59:44] Calculating protein q-values
[59:45] Number of genes identified at 1% FDR: 0 (precursor-level), 0 (protein-level) (inference performed using proteotypic peptides only)
[59:45] Quantification
[59:46] Precursors with scored PTMs at 1% FDR: 2835 out of 3144 considered
[59:46] Precursors with all scored PTM sites unoccupied at 1% FDR: 94863
[59:46] Precursors with PTMs localised (when required) with > 90% confidence: 2718 out of 2835
[59:47] Quantification information saved to /public/local/ProteoBench/HYE_Astral/LFQ_Astral_DIA_15min_50ng_Condition_B_REP3.raw.quant

[59:47] Cross-run analysis
[59:47] Reading quantification information: 6 files
[60:10] Target precursors at 1% global q-value: 130803
[60:10] Quantifying peptides
[61:25] Quantification parameters: 0.369134, 0.00139322, 0.00145866, 0.0468697, 0.0747889, 0.0844837, 0.300753, 0.113332, 0.140317, 0.114032, 0.0509545, 0.0589092, 0.208696, 0.0507051, 0.0564055, 0.0118823
[61:47] Assembling protein groups
[61:48] Quantifying proteins
[61:49] Calculating q-values for protein and gene groups
[61:51] Calculating global q-values for protein and gene groups
[61:51] Protein groups with global q-value <= 0.01: 11646
[61:54] Compressed report saved to /home/robbe/PB_output/results/test_run/HYE_Astral/diann_v2.5.0/report.parquet. Use R 'arrow' or Python 'PyArrow' package to process
[61:54] Stats report saved to /home/robbe/PB_output/results/test_run/HYE_Astral/diann_v2.5.0/report.stats.tsv
[61:54] Generating spectral library:
[61:57] 140821 target and 7716 decoy precursors saved
WARNING: 19889 precursors without any fragments annotated were skipped
[61:57] Spectral library saved to /home/robbe/PB_output/results/test_run/HYE_Astral/diann_v2.5.0/report-lib.parquet

