
DIA-NN 2.5.0 Academia  (Data-Independent Acquisition by Neural Networks)
Compiled on Apr 12 2026 10:45:33
Current date and time: Wed Aug  5 13:28:28 2026
Logical CPU cores: 128
/usr/diann-2.5.0/diann --f /public/local/ProteoBench/Entrapment_DIA/LFQ_Astral_DIA_15min_50ng_Human_01.raw --f /public/local/ProteoBench/Entrapment_DIA/LFQ_Astral_DIA_15min_50ng_Human_02.raw --f /public/local/ProteoBench/Entrapment_DIA/LFQ_Astral_DIA_15min_50ng_Human_03.raw --fasta /public/local/ProteoBench/fastas/ProteoBenchFASTA_Entrapment_Human_with_contaminants_entrapment_pep.fasta --out /home/robbe/PB_output/results/Entrapment_trying_bad_more/Entrapment_DIA/diann_v2.5.0/report.tsv --temp /home/robbe/PB_output/results/Entrapment_trying_bad_more/Entrapment_DIA/diann_v2.5.0 --threads 100 --missed-cleavages 0 --min-pep-len 7 --max-pep-len 50 --qvalue 0.01 --protein-qvalue 0.01 --min-pr-charge 1 --max-pr-charge 6 --min-pr-mz 380 --max-pr-mz 980 --min-fr-mz 150 --max-fr-mz 2000 --cut  --gen-spec-lib --predictor --mass-acc 25 --mass-acc-ms1 25 --unimod4 --var-mods 0 --gen-spec-lib --fasta-search --reanalyse --dg-keep-cterm 3 --dg-keep-nterm 3 --dg-min-shuffle 15.0 --dg-min-mut 40.0 --dg-max-mut 200.0 

Thread number set to 100
Maximum number of missed cleavages set to 0
Min peptide length set to 7
Max peptide length set to 50
Output will be filtered at 0.01 FDR
Output will be filtered at 0.01 protein-level FDR
Min precursor charge set to 1
Max precursor charge set to 6
Min precursor m/z set to 380
Max precursor m/z set to 980
Min fragment m/z set to 150
Max fragment m/z set to 2000
A spectral library will be generated
Deep learning will be used to generate a new in silico spectral library from peptides provided
Cysteine carbamidomethylation enabled as a fixed modification
Maximum number of variable modifications set to 0
A spectral library will be generated
DIA-NN will carry out FASTA digest for in silico lib generation
MBR enabled; .quant files will only be saved to disk during the first pass
Decoy generation will keep the last 3 amino acids of the target sequence
Decoy generation will keep the first 3 amino acids of the target sequence
Target minimum mass shift for shuffle decoys geneneration set to 15
Target minimum mass shift for mutated decoys geneneration set to 40
Target maximum mass shift for mutated decoys geneneration set to 200
Mass accuracy will be fixed to 2.5e-05 (MS2) and 2.5e-05 (MS1)
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

3 files will be processed
[0:00] Loading FASTA /public/local/ProteoBench/fastas/ProteoBenchFASTA_Entrapment_Human_with_contaminants_entrapment_pep.fasta
[0:27] Processing FASTA
[0:30] Assembling elution groups
[0:52] 5521504 precursors generated
[0:56] Gene names missing for some isoforms
[0:56] Library contains 2841736 proteins, and 0 genes
WARNING: no gene information in the FASTA or library: consider using --ids-to-names
[1:05] [1:15] [2:57] [3:11] [3:14] [3:16] Saving the library to /home/robbe/PB_output/results/Entrapment_trying_bad_more/Entrapment_DIA/diann_v2.5.0/report-lib.predicted.speclib
[3:22] Initialising library
[3:37] Loading spectral library /home/robbe/PB_output/results/Entrapment_trying_bad_more/Entrapment_DIA/diann_v2.5.0/report-lib.predicted.speclib
[3:40] Library annotated with sequence database(s): /public/local/ProteoBench/fastas/ProteoBenchFASTA_Entrapment_Human_with_contaminants_entrapment_pep.fasta
[3:41] Spectral library loaded: 2841736 protein isoforms, 2841736 protein groups and 5521504 precursors in 2841736 elution groups (targets and decoys).
[3:41] Loading protein annotations from FASTA /public/local/ProteoBench/fastas/ProteoBenchFASTA_Entrapment_Human_with_contaminants_entrapment_pep.fasta
[3:59] Annotating library proteins with information from the FASTA database
[4:03] Gene names missing for some isoforms
[4:03] Library contains 2841736 proteins, and 0 genes
WARNING: no gene information in the FASTA or library: consider using --ids-to-names
[4:08] Initialising library

First pass: generating a spectral library from DIA data

[4:22] File #1/3
[4:22] Loading run /public/local/ProteoBench/Entrapment_DIA/LFQ_Astral_DIA_15min_50ng_Human_01.raw
[4:43] Pre-processing...
[4:44] 2928 MS1 and 292883 MS2 scans in 976 (inferred) and 976 (encoded) cycles, 5517024 precursors in range
[4:44] Calibrating with mass accuracies 25 (MS1), 25 (MS2)
[4:54] RT window set to 1.56358
[4:54] Peak width: 2.964
[4:54] Scan window radius set to 6
[4:54] Recommended MS1 mass accuracy setting: 2.3 ppm
[5:01] Main search
[6:00] Removing low confidence identifications
[6:06] Removing interfering precursors
[6:13] Training neural networks on 167132 target and 141515 decoy PSMs
[6:33] Number of IDs at 0.01 FDR: 81668
[6:33] Calculating protein q-values
[6:34] Number of genes identified at 1% FDR: 0 (precursor-level), 0 (protein-level) (inference performed using proteotypic peptides only)
[6:34] Quantification
[6:35] Quantification information saved to /home/robbe/PB_output/results/Entrapment_trying_bad_more/Entrapment_DIA/diann_v2.5.0/_public_local_ProteoBench_Entrapment_DIA_LFQ_Astral_DIA_15min_50ng_Human_01_raw.quant

[6:35] File #2/3
[6:35] Loading run /public/local/ProteoBench/Entrapment_DIA/LFQ_Astral_DIA_15min_50ng_Human_02.raw
[6:52] Pre-processing...
[6:54] 2928 MS1 and 292914 MS2 scans in 976 (inferred) and 976 (encoded) cycles, 5517024 precursors in range
[6:54] Calibrating with mass accuracies 25 (MS1), 25 (MS2)
[7:01] RT window set to 1.56139
[7:01] Recommended MS1 mass accuracy setting: 2.3 ppm
[7:07] Main search
[7:52] Removing low confidence identifications
[7:57] Removing interfering precursors
[8:03] Training neural networks on 149437 target and 120907 decoy PSMs
[8:20] Number of IDs at 0.01 FDR: 80993
[8:20] Calculating protein q-values
[8:20] Number of genes identified at 1% FDR: 0 (precursor-level), 0 (protein-level) (inference performed using proteotypic peptides only)
[8:20] Quantification
[8:21] Quantification information saved to /home/robbe/PB_output/results/Entrapment_trying_bad_more/Entrapment_DIA/diann_v2.5.0/_public_local_ProteoBench_Entrapment_DIA_LFQ_Astral_DIA_15min_50ng_Human_02_raw.quant

[8:22] File #3/3
[8:22] Loading run /public/local/ProteoBench/Entrapment_DIA/LFQ_Astral_DIA_15min_50ng_Human_03.raw
[8:40] Pre-processing...
[8:41] 2927 MS1 and 292757 MS2 scans in 976 (inferred) and 976 (encoded) cycles, 5517024 precursors in range
[8:42] Calibrating with mass accuracies 25 (MS1), 25 (MS2)
[8:49] RT window set to 1.49611
[8:49] Recommended MS1 mass accuracy setting: 2.4 ppm
[8:55] Main search
[9:38] Removing low confidence identifications
[9:45] Removing interfering precursors
[9:51] Training neural networks on 163554 target and 138264 decoy PSMs
[10:10] Number of IDs at 0.01 FDR: 80670
[10:10] Calculating protein q-values
[10:10] Number of genes identified at 1% FDR: 0 (precursor-level), 0 (protein-level) (inference performed using proteotypic peptides only)
[10:11] Quantification
[10:12] Quantification information saved to /home/robbe/PB_output/results/Entrapment_trying_bad_more/Entrapment_DIA/diann_v2.5.0/_public_local_ProteoBench_Entrapment_DIA_LFQ_Astral_DIA_15min_50ng_Human_03_raw.quant

[10:12] Cross-run analysis
[10:12] Reading quantification information: 3 files
[10:27] Target precursors at 1% global q-value: 93470
[10:27] Quantifying peptides
[10:50] Assembling protein groups
[10:53] Quantifying proteins
[10:53] Calculating q-values for protein and gene groups
[10:54] Calculating global q-values for protein and gene groups
[10:54] Protein groups with global q-value <= 0.01: 85239
[10:56] Compressed report saved to /home/robbe/PB_output/results/Entrapment_trying_bad_more/Entrapment_DIA/diann_v2.5.0/report-first-pass.parquet. Use R 'arrow' or Python 'PyArrow' package to process
[10:56] Stats report saved to /home/robbe/PB_output/results/Entrapment_trying_bad_more/Entrapment_DIA/diann_v2.5.0/report-first-pass.stats.tsv
[10:56] Generating spectral library:
[10:57] 99998 target and 4496 decoy precursors saved
WARNING: 12056 precursors without any fragments annotated were skipped
[10:58] Spectral library saved to /home/robbe/PB_output/results/Entrapment_trying_bad_more/Entrapment_DIA/diann_v2.5.0/report-lib.parquet

[11:01] Loading spectral library /home/robbe/PB_output/results/Entrapment_trying_bad_more/Entrapment_DIA/diann_v2.5.0/report-lib.parquet
[11:02] Spectral library loaded: 95552 protein isoforms, 95552 protein groups and 104492 precursors in 95999 elution groups (targets and decoys).
[11:02] Loading protein annotations from FASTA /public/local/ProteoBench/fastas/ProteoBenchFASTA_Entrapment_Human_with_contaminants_entrapment_pep.fasta
[11:21] Annotating library proteins with information from the FASTA database
[11:22] Gene names missing for some isoforms
[11:22] Library contains 95552 proteins, and 0 genes
WARNING: no gene information in the FASTA or library: consider using --ids-to-names
[11:23] Initialising library
[11:24] Saving the library to /home/robbe/PB_output/results/Entrapment_trying_bad_more/Entrapment_DIA/diann_v2.5.0/report-lib.parquet.skyline.speclib


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

[11:24] File #1/3
[11:24] Loading run /public/local/ProteoBench/Entrapment_DIA/LFQ_Astral_DIA_15min_50ng_Human_01.raw
[11:44] Pre-processing...
[11:45] 2928 MS1 and 292883 MS2 scans in 976 (inferred) and 976 (encoded) cycles, 99998 precursors in range
[11:45] Calibrating with mass accuracies 25 (MS1), 25 (MS2)
[11:45] RT window set to 0.443011
[11:45] Recommended MS1 mass accuracy setting: 2.7 ppm
[11:46] Main search
[11:46] Removing low confidence identifications
[11:48] Removing interfering precursors
[11:49] Training neural networks on 84289 target and 47203 decoy PSMs
[11:56] Number of IDs at 0.01 FDR: 74151
[11:56] Calculating protein q-values
[11:56] Number of genes identified at 1% FDR: 0 (precursor-level), 0 (protein-level) (inference performed using proteotypic peptides only)
[11:56] Quantification

[11:57] File #2/3
[11:57] Loading run /public/local/ProteoBench/Entrapment_DIA/LFQ_Astral_DIA_15min_50ng_Human_02.raw
[12:17] Pre-processing...
[12:18] 2928 MS1 and 292914 MS2 scans in 976 (inferred) and 976 (encoded) cycles, 99998 precursors in range
[12:18] Calibrating with mass accuracies 25 (MS1), 25 (MS2)
[12:18] RT window set to 0.433588
[12:18] Recommended MS1 mass accuracy setting: 2.9 ppm
[12:18] Main search
[12:19] Removing low confidence identifications
[12:21] Removing interfering precursors
[12:21] Training neural networks on 84143 target and 46722 decoy PSMs
[12:28] Number of IDs at 0.01 FDR: 73863
[12:28] Calculating protein q-values
[12:28] Number of genes identified at 1% FDR: 0 (precursor-level), 0 (protein-level) (inference performed using proteotypic peptides only)
[12:28] Quantification

[12:29] File #3/3
[12:29] Loading run /public/local/ProteoBench/Entrapment_DIA/LFQ_Astral_DIA_15min_50ng_Human_03.raw
[12:50] Pre-processing...
[12:51] 2927 MS1 and 292757 MS2 scans in 976 (inferred) and 976 (encoded) cycles, 99998 precursors in range
[12:51] Calibrating with mass accuracies 25 (MS1), 25 (MS2)
[12:51] RT window set to 0.437393
[12:51] Recommended MS1 mass accuracy setting: 2.8 ppm
[12:51] Main search
[12:52] Removing low confidence identifications
[12:54] Removing interfering precursors
[12:54] Training neural networks on 84010 target and 45460 decoy PSMs
[13:01] Number of IDs at 0.01 FDR: 73740
[13:01] Calculating protein q-values
[13:01] Number of genes identified at 1% FDR: 0 (precursor-level), 0 (protein-level) (inference performed using proteotypic peptides only)
[13:01] Quantification

[13:02] Cross-run analysis
[13:02] Reading quantification information: 3 files
[13:03] Target precursors at 1% global q-value: 81790
[13:03] Quantifying peptides
WARNING: QuantUMS requires 6 or more runs for the optimisation of its hyperparameters to perform best.
[14:44] Quantification parameters: 0.292624, 0.0022517, 0.00133417, 0.0124681, 0.945926, 0.456019, 0.165186, 0.0974631, 0.0134395, 0.363208, 0.0535962, 0.0723823, 0.992669, 0.0530205, 0.0850181, 0.010009
[14:54] Quantifying proteins
[14:54] Calculating q-values for protein and gene groups
[14:54] Calculating global q-values for protein and gene groups
[14:54] Protein groups with global q-value <= 0.01: 74690
[14:55] Compressed report saved to /home/robbe/PB_output/results/Entrapment_trying_bad_more/Entrapment_DIA/diann_v2.5.0/report.parquet. Use R 'arrow' or Python 'PyArrow' package to process
[14:55] Stats report saved to /home/robbe/PB_output/results/Entrapment_trying_bad_more/Entrapment_DIA/diann_v2.5.0/report.stats.tsv

