
DIA-NN 2.3.0 Academia  (Data-Independent Acquisition by Neural Networks)
Compiled on Sep 26 2025 02:56:25
Current date and time: Wed Apr 22 00:35:10 2026
Logical CPU cores: 128
/home/robbe/bin/diann-2.3.0/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_v2.3.0/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
[0:17] 8103720 precursors generated
[0:17] Protein names missing for some isoforms
[0:17] Gene names missing for some isoforms
[0:17] Library contains 31680 proteins, and 0 genes
[0:23] [0:38] [7:05] [7:52] [7:55] [7:58] Saving the library to /home/robbe/PB_output/results/test_run/HYE_Astral/diann_v2.3.0/report-lib.predicted.speclib
[8:10] Initialising library
[8:25] Loading spectral library /home/robbe/PB_output/results/test_run/HYE_Astral/diann_v2.3.0/report-lib.predicted.speclib
[8:28] Library annotated with sequence database(s): /public/local/ProteoBench/fastas/ProteoBenchFASTA_MixedSpecies_HYE.fasta
[8:30] Spectral library loaded: 31832 protein isoforms, 43199 protein groups and 8103720 precursors in 3825450 elution groups.
[8:30] Loading protein annotations from FASTA /public/local/ProteoBench/fastas/ProteoBenchFASTA_MixedSpecies_HYE.fasta
[8:30] Annotating library proteins with information from the FASTA database
[8:30] Protein names missing for some isoforms
[8:30] Gene names missing for some isoforms
[8:30] Library contains 31680 proteins, and 0 genes
[8:36] Initialising library
WARNING: it is strongly recommended to enable MBR when analysing with a large library, if this is a quantitative analysis

[8:49] File #1/6
[8:49] Loading run /public/local/ProteoBench/HYE_Astral/LFQ_Astral_DIA_15min_50ng_Condition_A_REP1.raw
[10:09] Pre-processing...
[10:12] 2931 MS1 and 293271 MS2 scans in 977 (inferred) and 977 (encoded) cycles, 7009928 precursors in range
[10:13] Calibrating with mass accuracies 21 (MS1), 25 (MS2)
[10:30] RT window set to 1.25057
[10:30] Peak width: 2.94
[10:30] Scan window radius set to 6
[10:30] Recommended MS1 mass accuracy setting: 2.6 ppm
[10:40] Searching decoys
[11:36] Main search
[13:24] Removing low confidence identifications
[13:36] Removing interfering precursors
[13:45] Training neural networks on 156428 target and 101722 decoy PSMs
[14:21] Training neural networks on 156428 target and 98600 decoy PSMs
[14:47] IDs at 0.01 FDR: 94505
[14:48] Precursors at 1% peptidoform FDR: 92826
[14:50] Number of IDs at 0.01 FDR: 99943
[14:50] Calculating protein q-values
[14:51] Number of genes identified at 1% FDR: 0 (precursor-level), 0 (protein-level) (inference performed using proteotypic peptides only)
[14:51] Quantification
[14:52] Precursors with scored PTMs at 1% FDR: 2139 out of 2495 considered
[14:52] Precursors with all scored PTM sites unoccupied at 1% FDR: 92780
[14:52] Precursors with PTMs localised (when required) with > 90% confidence: 2054 out of 2139
[14:53] Quantification information saved to /public/local/ProteoBench/HYE_Astral/LFQ_Astral_DIA_15min_50ng_Condition_A_REP1.raw.quant

[14:54] File #2/6
[14:54] Loading run /public/local/ProteoBench/HYE_Astral/LFQ_Astral_DIA_15min_50ng_Condition_A_REP2.raw
[15:37] Pre-processing...
[15:40] 2933 MS1 and 293433 MS2 scans in 978 (inferred) and 978 (encoded) cycles, 7009928 precursors in range
[15:41] Calibrating with mass accuracies 21 (MS1), 25 (MS2)
[15:58] RT window set to 1.18423
[15:58] Recommended MS1 mass accuracy setting: 2.7 ppm
[16:07] Searching decoys
[16:56] Main search
[18:35] Removing low confidence identifications
[18:48] Removing interfering precursors
[18:57] Training neural networks on 159646 target and 106439 decoy PSMs
[19:29] Training neural networks on 159646 target and 103646 decoy PSMs
[20:01] IDs at 0.01 FDR: 96412
[20:02] Precursors at 1% peptidoform FDR: 94309
[20:03] Number of IDs at 0.01 FDR: 100991
[20:03] Calculating protein q-values
[20:03] Number of genes identified at 1% FDR: 0 (precursor-level), 0 (protein-level) (inference performed using proteotypic peptides only)
[20:03] Quantification
[20:05] Precursors with scored PTMs at 1% FDR: 2235 out of 2624 considered
[20:05] Precursors with all scored PTM sites unoccupied at 1% FDR: 93624
[20:05] Precursors with PTMs localised (when required) with > 90% confidence: 2156 out of 2235
[20:06] Quantification information saved to /public/local/ProteoBench/HYE_Astral/LFQ_Astral_DIA_15min_50ng_Condition_A_REP2.raw.quant

[20:06] File #3/6
[20:06] Loading run /public/local/ProteoBench/HYE_Astral/LFQ_Astral_DIA_15min_50ng_Condition_A_REP3.raw
[21:22] Pre-processing...
[21:26] 2932 MS1 and 293358 MS2 scans in 978 (inferred) and 978 (encoded) cycles, 7009928 precursors in range
[21:26] Calibrating with mass accuracies 21 (MS1), 25 (MS2)
[21:45] RT window set to 1.24575
[21:45] Recommended MS1 mass accuracy setting: 2.8 ppm
[21:56] Searching decoys
[22:59] Main search
[24:54] Removing low confidence identifications
[25:07] Removing interfering precursors
[25:16] Training neural networks on 159579 target and 106293 decoy PSMs
[25:54] Training neural networks on 159579 target and 103336 decoy PSMs
[26:26] IDs at 0.01 FDR: 95902
[26:27] Precursors at 1% peptidoform FDR: 93930
[26:29] Number of IDs at 0.01 FDR: 101500
[26:29] Calculating protein q-values
[26:30] Number of genes identified at 1% FDR: 0 (precursor-level), 0 (protein-level) (inference performed using proteotypic peptides only)
[26:30] Quantification
[26:31] Precursors with scored PTMs at 1% FDR: 2195 out of 2646 considered
[26:31] Precursors with all scored PTM sites unoccupied at 1% FDR: 93377
[26:31] Precursors with PTMs localised (when required) with > 90% confidence: 2112 out of 2195
[26:32] Quantification information saved to /public/local/ProteoBench/HYE_Astral/LFQ_Astral_DIA_15min_50ng_Condition_A_REP3.raw.quant

[26:32] File #4/6
[26:32] Loading run /public/local/ProteoBench/HYE_Astral/LFQ_Astral_DIA_15min_50ng_Condition_B_REP1.raw
[27:26] Pre-processing...
[27:29] 2933 MS1 and 293382 MS2 scans in 978 (inferred) and 978 (encoded) cycles, 7009928 precursors in range
[27:30] Calibrating with mass accuracies 21 (MS1), 25 (MS2)
[27:48] RT window set to 1.35416
[27:48] Recommended MS1 mass accuracy setting: 3 ppm
[28:00] Searching decoys
[29:07] Main search
[31:19] Removing low confidence identifications
[31:33] Removing interfering precursors
[31:41] Training neural networks on 163039 target and 110063 decoy PSMs
[32:20] Training neural networks on 163039 target and 106888 decoy PSMs
[32:52] IDs at 0.01 FDR: 97013
[32:53] Precursors at 1% peptidoform FDR: 94848
[32:55] Number of IDs at 0.01 FDR: 103064
[32:55] Calculating protein q-values
[32:56] Number of genes identified at 1% FDR: 0 (precursor-level), 0 (protein-level) (inference performed using proteotypic peptides only)
[32:56] Quantification
[32:57] Precursors with scored PTMs at 1% FDR: 2771 out of 3255 considered
[32:57] Precursors with all scored PTM sites unoccupied at 1% FDR: 93709
[32:57] Precursors with PTMs localised (when required) with > 90% confidence: 2656 out of 2771
[32:58] Quantification information saved to /public/local/ProteoBench/HYE_Astral/LFQ_Astral_DIA_15min_50ng_Condition_B_REP1.raw.quant

[32:59] File #5/6
[32:59] Loading run /public/local/ProteoBench/HYE_Astral/LFQ_Astral_DIA_15min_50ng_Condition_B_REP2.raw
[33:46] Pre-processing...
[33:49] 2933 MS1 and 293330 MS2 scans in 978 (inferred) and 978 (encoded) cycles, 7009928 precursors in range
[33:50] Calibrating with mass accuracies 21 (MS1), 25 (MS2)
[34:09] RT window set to 1.44596
[34:09] Recommended MS1 mass accuracy setting: 2.8 ppm
[34:20] Searching decoys
[35:28] Main search
[37:42] Removing low confidence identifications
[37:57] Removing interfering precursors
[38:06] Training neural networks on 161707 target and 108110 decoy PSMs
[38:41] Training neural networks on 161707 target and 105202 decoy PSMs
[39:11] IDs at 0.01 FDR: 96943
[39:12] Precursors at 1% peptidoform FDR: 95327
[39:13] Number of IDs at 0.01 FDR: 102809
[39:13] Calculating protein q-values
[39:14] Number of genes identified at 1% FDR: 0 (precursor-level), 0 (protein-level) (inference performed using proteotypic peptides only)
[39:14] Quantification
[39:15] Precursors with scored PTMs at 1% FDR: 2879 out of 3239 considered
[39:15] Precursors with all scored PTM sites unoccupied at 1% FDR: 94527
[39:15] Precursors with PTMs localised (when required) with > 90% confidence: 2768 out of 2879
[39:17] Quantification information saved to /public/local/ProteoBench/HYE_Astral/LFQ_Astral_DIA_15min_50ng_Condition_B_REP2.raw.quant

[39:17] File #6/6
[39:17] Loading run /public/local/ProteoBench/HYE_Astral/LFQ_Astral_DIA_15min_50ng_Condition_B_REP3.raw
[40:05] Pre-processing...
[40:07] 2934 MS1 and 293446 MS2 scans in 978 (inferred) and 978 (encoded) cycles, 7009928 precursors in range
[40:08] Calibrating with mass accuracies 21 (MS1), 25 (MS2)
[40:24] RT window set to 1.31698
[40:24] Recommended MS1 mass accuracy setting: 3 ppm
[40:34] Searching decoys
[41:33] Main search
[43:24] Removing low confidence identifications
[43:36] Removing interfering precursors
[43:44] Training neural networks on 162637 target and 108422 decoy PSMs
[44:17] Training neural networks on 162637 target and 105239 decoy PSMs
[44:44] IDs at 0.01 FDR: 97377
[44:44] Precursors at 1% peptidoform FDR: 95061
[44:46] Number of IDs at 0.01 FDR: 102479
[44:46] Calculating protein q-values
[44:46] Number of genes identified at 1% FDR: 0 (precursor-level), 0 (protein-level) (inference performed using proteotypic peptides only)
[44:46] Quantification
[44:48] Precursors with scored PTMs at 1% FDR: 2717 out of 3247 considered
[44:48] Precursors with all scored PTM sites unoccupied at 1% FDR: 93877
[44:48] Precursors with PTMs localised (when required) with > 90% confidence: 2609 out of 2717
[44:49] Quantification information saved to /public/local/ProteoBench/HYE_Astral/LFQ_Astral_DIA_15min_50ng_Condition_B_REP3.raw.quant

[44:49] Cross-run analysis
[44:49] Reading quantification information: 6 files
[45:10] Quantifying peptides
[46:00] Quantification parameters: 0.369371, 0.00138789, 0.00145498, 0.0331457, 0.0546092, 0.0761971, 0.313345, 0.105639, 0.139967, 0.125022, 0.0509304, 0.0598172, 0.218307, 0.0508752, 0.0576086, 0.0120149
[46:14] Assembling protein groups
[46:16] Quantifying proteins
[46:17] Calculating q-values for protein and gene groups
[46:18] Calculating global q-values for protein and gene groups
[46:18] Protein groups with global q-value <= 0.01: 11183
[46:21] Compressed report saved to /home/robbe/PB_output/results/test_run/HYE_Astral/diann_v2.3.0/report.parquet. Use R 'arrow' or Python 'PyArrow' package to process
[46:21] Stats report saved to /home/robbe/PB_output/results/test_run/HYE_Astral/diann_v2.3.0/report.stats.tsv
[46:22] Generating spectral library:
[46:24] 119144 target and 1109 decoy precursors saved
WARNING: 8547 precursors without any fragments annotated were skipped
[46:24] Spectral library saved to /home/robbe/PB_output/results/test_run/HYE_Astral/diann_v2.3.0/report-lib.parquet

