
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 12:26:59 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/Entrapment_DIA/diann_v2.5.0/report.tsv --temp /home/robbe/PB_output/results/Entrapment_trying_bad/Entrapment_DIA/diann_v2.5.0 --threads 100 --missed-cleavages 1 --min-pep-len 6 --max-pep-len 40 --qvalue 0.01 --protein-qvalue 0.01 --min-pr-charge 1 --max-pr-charge 5 --min-pr-mz 380 --max-pr-mz 980 --min-fr-mz 150 --max-fr-mz 2000 --cut  --gen-spec-lib --predictor --mass-acc 20 --mass-acc-ms1 20 --unimod4 --var-mods 0 --gen-spec-lib --fasta-search --reanalyse --dg-keep-cterm 2 --dg-min-shuffle 2.0 --dg-min-mut 7.0 --dg-max-mut 25.0 

Thread number set to 100
Maximum number of missed cleavages set to 1
Min peptide length set to 6
Max peptide length set to 40
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 5
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 2 amino acids of the target sequence
Target minimum mass shift for shuffle decoys geneneration set to 2
Target minimum mass shift for mutated decoys geneneration set to 7
Target maximum mass shift for mutated decoys geneneration set to 25
Mass accuracy will be fixed to 2e-05 (MS2) and 2e-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
[0:31] Processing FASTA
[0:34] Assembling elution groups
[1:03] 5521504 precursors generated
[1:08] Gene names missing for some isoforms
[1:08] Library contains 2841736 proteins, and 0 genes
WARNING: no gene information in the FASTA or library: consider using --ids-to-names
[1:22] [1:38] [4:43] [5:19] [5:25] [5:30] Saving the library to /home/robbe/PB_output/results/Entrapment_trying_bad/Entrapment_DIA/diann_v2.5.0/report-lib.predicted.speclib
[5:43] Initialising library
[6:23] Loading spectral library /home/robbe/PB_output/results/Entrapment_trying_bad/Entrapment_DIA/diann_v2.5.0/report-lib.predicted.speclib
[6:31] Library annotated with sequence database(s): /public/local/ProteoBench/fastas/ProteoBenchFASTA_Entrapment_Human_with_contaminants_entrapment_pep.fasta
[6:33] Spectral library loaded: 2841736 protein isoforms, 2841736 protein groups and 5521504 precursors in 2841736 elution groups (targets and decoys).
[6:33] Loading protein annotations from FASTA /public/local/ProteoBench/fastas/ProteoBenchFASTA_Entrapment_Human_with_contaminants_entrapment_pep.fasta
[7:16] Annotating library proteins with information from the FASTA database
[7:25] Gene names missing for some isoforms
[7:25] Library contains 2841736 proteins, and 0 genes
WARNING: no gene information in the FASTA or library: consider using --ids-to-names
[7:36] Initialising library

First pass: generating a spectral library from DIA data

[8:02] File #1/3
[8:02] Loading run /public/local/ProteoBench/Entrapment_DIA/LFQ_Astral_DIA_15min_50ng_Human_01.raw
[8:52] Pre-processing...
[8:54] 2928 MS1 and 292883 MS2 scans in 976 (inferred) and 976 (encoded) cycles, 5517024 precursors in range
[8:55] Calibrating with mass accuracies 21 (MS1), 25 (MS2)
[9:11] RT window set to 1.58636
[9:11] Peak width: 2.944
[9:11] Scan window radius set to 6
[9:11] Recommended MS1 mass accuracy setting: 2.4 ppm
[9:22] Main search
[10:43] Removing low confidence identifications
[10:55] Removing interfering precursors
[11:07] Training neural networks on 185913 target and 159902 decoy PSMs
[11:50] Number of IDs at 0.01 FDR: 80284
[11:50] Calculating protein q-values
[11:51] Number of genes identified at 1% FDR: 0 (precursor-level), 0 (protein-level) (inference performed using proteotypic peptides only)
[11:51] Quantification
[11:54] Quantification information saved to /home/robbe/PB_output/results/Entrapment_trying_bad/Entrapment_DIA/diann_v2.5.0/_public_local_ProteoBench_Entrapment_DIA_LFQ_Astral_DIA_15min_50ng_Human_01_raw.quant

[11:54] File #2/3
[11:54] Loading run /public/local/ProteoBench/Entrapment_DIA/LFQ_Astral_DIA_15min_50ng_Human_02.raw
[12:57] Pre-processing...
[12:59] 2928 MS1 and 292914 MS2 scans in 976 (inferred) and 976 (encoded) cycles, 5517024 precursors in range
[13:00] Calibrating with mass accuracies 20 (MS1), 25 (MS2)
[13:15] RT window set to 1.62502
[13:15] Recommended MS1 mass accuracy setting: 2.5 ppm
[13:25] Main search
[14:42] Removing low confidence identifications
[14:53] Removing interfering precursors
[15:03] Training neural networks on 184781 target and 157473 decoy PSMs
[15:38] Number of IDs at 0.01 FDR: 80055
[15:38] Calculating protein q-values
[15:39] Number of genes identified at 1% FDR: 0 (precursor-level), 0 (protein-level) (inference performed using proteotypic peptides only)
[15:39] Quantification
[15:41] Quantification information saved to /home/robbe/PB_output/results/Entrapment_trying_bad/Entrapment_DIA/diann_v2.5.0/_public_local_ProteoBench_Entrapment_DIA_LFQ_Astral_DIA_15min_50ng_Human_02_raw.quant

[15:41] File #3/3
[15:41] Loading run /public/local/ProteoBench/Entrapment_DIA/LFQ_Astral_DIA_15min_50ng_Human_03.raw
[16:18] Pre-processing...
[16:19] 2927 MS1 and 292757 MS2 scans in 976 (inferred) and 976 (encoded) cycles, 5517024 precursors in range
[16:20] Calibrating with mass accuracies 20 (MS1), 25 (MS2)
[16:35] RT window set to 1.51606
[16:35] Recommended MS1 mass accuracy setting: 2.4 ppm
[16:43] Main search
[17:23] Removing low confidence identifications
[17:29] Removing interfering precursors
[17:37] Training neural networks on 185610 target and 158997 decoy PSMs
[18:03] Number of IDs at 0.01 FDR: 79884
[18:03] Calculating protein q-values
[18:03] Number of genes identified at 1% FDR: 0 (precursor-level), 0 (protein-level) (inference performed using proteotypic peptides only)
[18:04] Quantification
[18:05] Quantification information saved to /home/robbe/PB_output/results/Entrapment_trying_bad/Entrapment_DIA/diann_v2.5.0/_public_local_ProteoBench_Entrapment_DIA_LFQ_Astral_DIA_15min_50ng_Human_03_raw.quant

[18:05] Cross-run analysis
[18:05] Reading quantification information: 3 files
[18:15] Target precursors at 1% global q-value: 91771
[18:15] Quantifying peptides
[18:37] Assembling protein groups
[18:40] Quantifying proteins
[18:41] Calculating q-values for protein and gene groups
[18:42] Calculating global q-values for protein and gene groups
[18:42] Protein groups with global q-value <= 0.01: 83243
[18:44] Compressed report saved to /home/robbe/PB_output/results/Entrapment_trying_bad/Entrapment_DIA/diann_v2.5.0/report-first-pass.parquet. Use R 'arrow' or Python 'PyArrow' package to process
[18:44] Stats report saved to /home/robbe/PB_output/results/Entrapment_trying_bad/Entrapment_DIA/diann_v2.5.0/report-first-pass.stats.tsv
[18:44] Generating spectral library:
[18:45] 99616 target and 4591 decoy precursors saved
WARNING: 12135 precursors without any fragments annotated were skipped
[18:46] Spectral library saved to /home/robbe/PB_output/results/Entrapment_trying_bad/Entrapment_DIA/diann_v2.5.0/report-lib.parquet

[18:49] Loading spectral library /home/robbe/PB_output/results/Entrapment_trying_bad/Entrapment_DIA/diann_v2.5.0/report-lib.parquet
[18:51] Spectral library loaded: 95109 protein isoforms, 95109 protein groups and 104207 precursors in 95557 elution groups (targets and decoys).
[18:51] Loading protein annotations from FASTA /public/local/ProteoBench/fastas/ProteoBenchFASTA_Entrapment_Human_with_contaminants_entrapment_pep.fasta
[19:22] Annotating library proteins with information from the FASTA database
[19:24] Gene names missing for some isoforms
[19:24] Library contains 95109 proteins, and 0 genes
WARNING: no gene information in the FASTA or library: consider using --ids-to-names
[19:24] Initialising library
[19:26] Saving the library to /home/robbe/PB_output/results/Entrapment_trying_bad/Entrapment_DIA/diann_v2.5.0/report-lib.parquet.skyline.speclib


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

[19:26] File #1/3
[19:26] Loading run /public/local/ProteoBench/Entrapment_DIA/LFQ_Astral_DIA_15min_50ng_Human_01.raw
[19:44] Pre-processing...
[19:44] 2928 MS1 and 292883 MS2 scans in 976 (inferred) and 976 (encoded) cycles, 99616 precursors in range
[19:44] Calibrating with mass accuracies 21 (MS1), 25 (MS2)
[19:45] RT window set to 0.452828
[19:45] Recommended MS1 mass accuracy setting: 2.5 ppm
[19:45] Main search
[19:46] Removing low confidence identifications
[19:47] Removing interfering precursors
[19:48] Training neural networks on 84487 target and 47035 decoy PSMs
[19:59] Number of IDs at 0.01 FDR: 72231
[19:59] Calculating protein q-values
[19:59] Number of genes identified at 1% FDR: 0 (precursor-level), 0 (protein-level) (inference performed using proteotypic peptides only)
[19:59] Quantification

[20:00] File #2/3
[20:00] Loading run /public/local/ProteoBench/Entrapment_DIA/LFQ_Astral_DIA_15min_50ng_Human_02.raw
[20:14] Pre-processing...
[20:15] 2928 MS1 and 292914 MS2 scans in 976 (inferred) and 976 (encoded) cycles, 99616 precursors in range
[20:15] Calibrating with mass accuracies 20 (MS1), 25 (MS2)
[20:15] RT window set to 0.451483
[20:15] Recommended MS1 mass accuracy setting: 2.7 ppm
[20:15] Main search
[20:16] Removing low confidence identifications
[20:18] Removing interfering precursors
[20:18] Training neural networks on 84491 target and 46741 decoy PSMs
[20:25] Number of IDs at 0.01 FDR: 72066
[20:25] Calculating protein q-values
[20:25] Number of genes identified at 1% FDR: 0 (precursor-level), 0 (protein-level) (inference performed using proteotypic peptides only)
[20:25] Quantification

[20:26] File #3/3
[20:26] Loading run /public/local/ProteoBench/Entrapment_DIA/LFQ_Astral_DIA_15min_50ng_Human_03.raw
[20:43] Pre-processing...
[20:44] 2927 MS1 and 292757 MS2 scans in 976 (inferred) and 976 (encoded) cycles, 99616 precursors in range
[20:44] Calibrating with mass accuracies 20 (MS1), 25 (MS2)
[20:44] RT window set to 0.459309
[20:44] Recommended MS1 mass accuracy setting: 2.5 ppm
[20:44] Main search
[20:45] Removing low confidence identifications
[20:47] Removing interfering precursors
[20:47] Training neural networks on 84317 target and 47039 decoy PSMs
[20:54] Number of IDs at 0.01 FDR: 71327
[20:54] Calculating protein q-values
[20:54] Number of genes identified at 1% FDR: 0 (precursor-level), 0 (protein-level) (inference performed using proteotypic peptides only)
[20:54] Quantification

[20:55] Cross-run analysis
[20:55] Reading quantification information: 3 files
[20:56] Target precursors at 1% global q-value: 80844
[20:56] Quantifying peptides
WARNING: QuantUMS requires 6 or more runs for the optimisation of its hyperparameters to perform best.
[21:32] Quantification parameters: 0.291098, 0.00226389, 0.0013713, 0.0118564, 0.0117331, 0.0117995, 0.140463, 0.129305, 0.104889, 0.0142021, 0.0473371, 0.0372722, 0.141669, 0.050428, 0.0562333, 0.0114956
[21:42] Quantifying proteins
[21:42] Calculating q-values for protein and gene groups
[21:42] Calculating global q-values for protein and gene groups
[21:42] Protein groups with global q-value <= 0.01: 73899
[21:43] Compressed report saved to /home/robbe/PB_output/results/Entrapment_trying_bad/Entrapment_DIA/diann_v2.5.0/report.parquet. Use R 'arrow' or Python 'PyArrow' package to process
[21:43] Stats report saved to /home/robbe/PB_output/results/Entrapment_trying_bad/Entrapment_DIA/diann_v2.5.0/report.stats.tsv

