
DIA-NN 2.3.1 Academia  (Data-Independent Acquisition by Neural Networks)
Compiled on Dec  5 2025 10:52:39
Current date and time: Tue Jun 23 11:01:22 2026
CPU: AuthenticAMD AMD Ryzen 9 5950X 16-Core Processor
SIMD instructions: AVX AVX2 FMA SSE4.1 SSE4.2 SSE4a 
Logical CPU cores: 32
57Gb out of 127Gb RAM is free
diann.exe --f C:\Users\cajac\Documents\ProteoBench\Entrapment_Runs\LFQ_Astral_DIA_15min_50ng_Human_01.raw  --f C:\Users\cajac\Documents\ProteoBench\Entrapment_Runs\LFQ_Astral_DIA_15min_50ng_Human_02.raw  --f C:\Users\cajac\Documents\ProteoBench\Entrapment_Runs\LFQ_Astral_DIA_15min_50ng_Human_03.raw  --lib C:\Users\cajac\Documents\ProteoBench\Entrapment_Runs\DIANN_output\DIANN_speclibs\no_Mexcision_no_varmods_cutempty_7_30\no_Mexc_no_varmods_cutempty_7_30.predicted.speclib --threads 16 --verbose 1 --out C:\Users\cajac\Documents\ProteoBench\Entrapment_Runs\DIANN_output\DIANN_search_results\report_no_Mexc_no_varmods_7_30_cutempty.parquet --qvalue 0.01 --matrices --out-lib C:\Users\cajac\Documents\ProteoBench\Entrapment_Runs\DIANN_output\DIANN_speclibs\no_Mexcision_no_varmods_cutempty_7_30\no_Mexc_no_varmods_cutempty_7_30.parquet --gen-spec-lib --fasta C:\Users\cajac\Documents\ProteoBench\Entrapment_Runs\FDRBench_output\ProteoBenchFASTA_Entrapment_Human_with_contaminants_entrapment_pep.fasta --min-pep-len 7 --max-pep-len 30 --min-pr-mz 300 --max-pr-mz 1800 --min-pr-charge 1 --max-pr-charge 4 --min-fr-mz 200 --max-fr-mz 1800 --cut K*,R* --missed-cleavages 1 --unimod4 --reanalyse --rt-profiling --cut 

Thread number set to 16
Output will be filtered at 0.01 FDR
Precursor/protein x samples expression level matrices will be saved along with the main report
A spectral library will be generated
Min peptide length set to 7
Max peptide length set to 30
Min precursor m/z set to 300
Max precursor m/z set to 1800
Min precursor charge set to 1
Max precursor charge set to 4
Min fragment m/z set to 200
Max fragment m/z set to 1800
In silico digest will involve cuts at K*,R*
Maximum number of missed cleavages set to 1
Cysteine carbamidomethylation enabled as a fixed modification
MBR enabled; .quant files will only be saved to disk during the first pass
The spectral library (if generated) will retain the original spectra but will include empirically-aligned RTs
DIA-NN will automatically optimise the mass accuracy for the first run of the experiment, use this mode for preliminary analyses only

3 files will be processed
[0:00] Loading spectral library C:\Users\cajac\Documents\ProteoBench\Entrapment_Runs\DIANN_output\DIANN_speclibs\no_Mexcision_no_varmods_cutempty_7_30\no_Mexc_no_varmods_cutempty_7_30.predicted.speclib
[0:12] Library annotated with sequence database(s): C:\Users\cajac\Documents\ProteoBench\Entrapment_Runs\FDRBench_output\ProteoBenchFASTA_Entrapment_Human_with_contaminants_entrapment_pep.fasta
[0:14] Spectral library loaded: 2692562 protein isoforms, 2692562 protein groups and 8649692 precursors in 2692562 elution groups.
[0:14] Loading protein annotations from FASTA C:\Users\cajac\Documents\ProteoBench\Entrapment_Runs\FDRBench_output\ProteoBenchFASTA_Entrapment_Human_with_contaminants_entrapment_pep.fasta
[1:03] Annotating library proteins with information from the FASTA database
[1:06] Gene names missing for some isoforms
[1:06] Library contains 2692562 proteins, and 0 genes
WARNING: no gene information in the FASTA or library: consider using --ids-to-names
[1:16] Initialising library

First pass: generating a spectral library from DIA data

[1:35] File #1/3
[1:35] Loading run C:\Users\cajac\Documents\ProteoBench\Entrapment_Runs\LFQ_Astral_DIA_15min_50ng_Human_01.raw
[2:12] Pre-processing...
[2:22] 2928 MS1 and 292883 MS2 scans in 976 (inferred) and 976 (encoded) cycles, 5370648 precursors in range
[2:24] Calibrating with mass accuracies 21 (MS1), 25 (MS2)
[3:09] RT window set to 1.49073
[3:09] Peak width: 2.856
[3:09] Scan window radius set to 6
[3:09] Recommended MS1 mass accuracy setting: 2.2 ppm
[4:20] Optimised mass accuracy: 7 ppm
[4:34] Main search
[7:27] Removing low confidence identifications
[7:44] Removing interfering precursors
[7:54] Training neural networks on 162322 target and 100568 decoy PSMs
[9:45] IDs at 0.01 FDR: 81701
[9:48] Number of IDs at 0.01 FDR: 81701
[9:48] Calculating protein q-values
[9:49] Number of genes identified at 1% FDR: 0 (precursor-level), 0 (protein-level) (inference performed using proteotypic peptides only)
[9:49] Quantification
[9:52] Quantification information saved to C:\Users\cajac\Documents\ProteoBench\Entrapment_Runs\LFQ_Astral_DIA_15min_50ng_Human_01.raw.quant

[9:52] File #2/3
[9:52] Loading run C:\Users\cajac\Documents\ProteoBench\Entrapment_Runs\LFQ_Astral_DIA_15min_50ng_Human_02.raw
[10:06] Pre-processing...
[10:17] 2928 MS1 and 292914 MS2 scans in 976 (inferred) and 976 (encoded) cycles, 5370648 precursors in range
[10:18] Calibrating with mass accuracies 20 (MS1), 25 (MS2)
[11:06] RT window set to 1.56762
[11:07] Recommended MS1 mass accuracy setting: 2.3 ppm
[11:22] Main search
[14:08] Removing low confidence identifications
[14:26] Removing interfering precursors
[14:41] Training neural networks on 163437 target and 100620 decoy PSMs
[16:25] IDs at 0.01 FDR: 82781
[16:27] Number of IDs at 0.01 FDR: 82781
[16:27] Calculating protein q-values
[16:27] Number of genes identified at 1% FDR: 0 (precursor-level), 0 (protein-level) (inference performed using proteotypic peptides only)
[16:28] Quantification
[16:30] Quantification information saved to C:\Users\cajac\Documents\ProteoBench\Entrapment_Runs\LFQ_Astral_DIA_15min_50ng_Human_02.raw.quant

[16:31] File #3/3
[16:31] Loading run C:\Users\cajac\Documents\ProteoBench\Entrapment_Runs\LFQ_Astral_DIA_15min_50ng_Human_03.raw
[16:54] Pre-processing...
[17:06] 2927 MS1 and 292757 MS2 scans in 976 (inferred) and 976 (encoded) cycles, 5370648 precursors in range
[17:07] Calibrating with mass accuracies 20 (MS1), 25 (MS2)
[17:57] RT window set to 1.4467
[17:57] Recommended MS1 mass accuracy setting: 2.2 ppm
[18:11] Main search
[20:55] Removing low confidence identifications
[21:10] Removing interfering precursors
[21:23] Training neural networks on 162512 target and 100309 decoy PSMs
[22:57] IDs at 0.01 FDR: 81529
[23:00] Number of IDs at 0.01 FDR: 81529
[23:00] Calculating protein q-values
[23:00] Number of genes identified at 1% FDR: 0 (precursor-level), 0 (protein-level) (inference performed using proteotypic peptides only)
[23:00] Quantification
[23:03] Quantification information saved to C:\Users\cajac\Documents\ProteoBench\Entrapment_Runs\LFQ_Astral_DIA_15min_50ng_Human_03.raw.quant

[23:03] Cross-run analysis
[23:03] Reading quantification information: 3 files
[23:24] Quantifying peptides
[23:58] Assembling protein groups
[24:00] Quantifying proteins
[24:00] Calculating q-values for protein and gene groups
[24:01] Calculating global q-values for protein and gene groups
[24:01] Protein groups with global q-value <= 0.01: 85140
[24:02] Compressed report saved to C:\Users\cajac\Documents\ProteoBench\Entrapment_Runs\DIANN_output\DIANN_search_results\report_no_Mexc_no_varmods_7_30_cutempty-first-pass.parquet. Use R 'arrow' or Python 'PyArrow' package to process
[24:02] Saving precursor levels matrix
[24:02] Precursor levels matrix (1% precursor and protein group FDR) saved to C:\Users\cajac\Documents\ProteoBench\Entrapment_Runs\DIANN_output\DIANN_search_results\report_no_Mexc_no_varmods_7_30_cutempty-first-pass.pr_matrix.tsv.
[24:02] Manifest saved to C:\Users\cajac\Documents\ProteoBench\Entrapment_Runs\DIANN_output\DIANN_search_results\report_no_Mexc_no_varmods_7_30_cutempty-first-pass.manifest.txt
[24:02] Stats report saved to C:\Users\cajac\Documents\ProteoBench\Entrapment_Runs\DIANN_output\DIANN_search_results\report_no_Mexc_no_varmods_7_30_cutempty-first-pass.stats.tsv
[24:02] Generating spectral library:
[24:04] 93698 target and 920 decoy precursors saved
[24:04] Spectral library saved to C:\Users\cajac\Documents\ProteoBench\Entrapment_Runs\DIANN_output\DIANN_speclibs\no_Mexcision_no_varmods_cutempty_7_30\no_Mexc_no_varmods_cutempty_7_30.parquet

[24:05] Loading spectral library C:\Users\cajac\Documents\ProteoBench\Entrapment_Runs\DIANN_output\DIANN_speclibs\no_Mexcision_no_varmods_cutempty_7_30\no_Mexc_no_varmods_cutempty_7_30.parquet
[24:07] Spectral library loaded: 86661 protein isoforms, 86661 protein groups and 94617 precursors in 86686 elution groups.
[24:07] Loading protein annotations from FASTA C:\Users\cajac\Documents\ProteoBench\Entrapment_Runs\FDRBench_output\ProteoBenchFASTA_Entrapment_Human_with_contaminants_entrapment_pep.fasta
[25:16] Annotating library proteins with information from the FASTA database
[25:16] Gene names missing for some isoforms
[25:16] Library contains 86661 proteins, and 0 genes
WARNING: no gene information in the FASTA or library: consider using --ids-to-names
[25:16] Initialising library
[25:19] Saving the library to C:\Users\cajac\Documents\ProteoBench\Entrapment_Runs\DIANN_output\DIANN_speclibs\no_Mexcision_no_varmods_cutempty_7_30\no_Mexc_no_varmods_cutempty_7_30.parquet.skyline.speclib


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

[25:19] File #1/3
[25:19] Loading run C:\Users\cajac\Documents\ProteoBench\Entrapment_Runs\LFQ_Astral_DIA_15min_50ng_Human_01.raw
[25:40] Pre-processing...
[25:41] 2928 MS1 and 292883 MS2 scans in 976 (inferred) and 976 (encoded) cycles, 93698 precursors in range
[25:41] Calibrating with mass accuracies 21 (MS1), 25 (MS2)
[25:43] RT window set to 0.428753
[25:43] Recommended MS1 mass accuracy setting: 2.4 ppm
[25:43] Main search
[25:46] Removing low confidence identifications
[25:51] Removing interfering precursors
[25:52] Training neural networks on 85521 target and 46501 decoy PSMs
[26:44] IDs at 0.01 FDR: 87706
[26:45] Number of IDs at 0.01 FDR: 87729
[26:45] Calculating protein q-values
[26:45] Number of genes identified at 1% FDR: 0 (precursor-level), 0 (protein-level) (inference performed using proteotypic peptides only)
[26:45] Quantification

[26:47] File #2/3
[26:47] Loading run C:\Users\cajac\Documents\ProteoBench\Entrapment_Runs\LFQ_Astral_DIA_15min_50ng_Human_02.raw
[27:01] Pre-processing...
[27:02] 2928 MS1 and 292914 MS2 scans in 976 (inferred) and 976 (encoded) cycles, 93698 precursors in range
[27:02] Calibrating with mass accuracies 20 (MS1), 25 (MS2)
[27:03] RT window set to 0.429435
[27:03] Recommended MS1 mass accuracy setting: 2.7 ppm
[27:04] Main search
[27:07] Removing low confidence identifications
[27:11] Removing interfering precursors
[27:12] Training neural networks on 85588 target and 46636 decoy PSMs
[28:03] IDs at 0.01 FDR: 87642
[28:04] Number of IDs at 0.01 FDR: 87642
[28:04] Calculating protein q-values
[28:04] Number of genes identified at 1% FDR: 0 (precursor-level), 0 (protein-level) (inference performed using proteotypic peptides only)
[28:04] Quantification

[28:06] File #3/3
[28:06] Loading run C:\Users\cajac\Documents\ProteoBench\Entrapment_Runs\LFQ_Astral_DIA_15min_50ng_Human_03.raw
[28:20] Pre-processing...
[28:21] 2927 MS1 and 292757 MS2 scans in 976 (inferred) and 976 (encoded) cycles, 93698 precursors in range
[28:21] Calibrating with mass accuracies 20 (MS1), 25 (MS2)
[28:22] RT window set to 0.427269
[28:22] Recommended MS1 mass accuracy setting: 2.7 ppm
[28:22] Main search
[28:25] Removing low confidence identifications
[28:29] Removing interfering precursors
[28:31] Training neural networks on 85421 target and 46306 decoy PSMs
[29:13] IDs at 0.01 FDR: 87311
[29:14] Number of IDs at 0.01 FDR: 87315
[29:14] Calculating protein q-values
[29:14] Number of genes identified at 1% FDR: 0 (precursor-level), 0 (protein-level) (inference performed using proteotypic peptides only)
[29:14] Quantification

[29:15] Cross-run analysis
[29:15] Reading quantification information: 3 files
[29:16] Quantifying peptides
WARNING: QuantUMS requires 6 or more runs for the optimisation of its hyperparameters to perform best.
[33:06] Quantification parameters: 0.348105, 0.00214552, 0.00161385, 0.599389, 0.530161, 0.520505, 0.0113209, 0.537516, 0.380838, 0.710343, 0.0595221, 0.12441, 0.8233, 0.0481904, 0.0444435, 0.00988654
[33:27] Quantifying proteins
[33:27] Calculating q-values for protein and gene groups
[33:27] Calculating global q-values for protein and gene groups
[33:27] Protein groups with global q-value <= 0.01: 83153
[33:29] Compressed report saved to C:\Users\cajac\Documents\ProteoBench\Entrapment_Runs\DIANN_output\DIANN_search_results\report_no_Mexc_no_varmods_7_30_cutempty.parquet. Use R 'arrow' or Python 'PyArrow' package to process
[33:29] Saving precursor levels matrix
[33:29] Precursor levels matrix (1% precursor and protein group FDR) saved to C:\Users\cajac\Documents\ProteoBench\Entrapment_Runs\DIANN_output\DIANN_search_results\report_no_Mexc_no_varmods_7_30_cutempty.pr_matrix.tsv.
[33:29] Saving protein group levels matrix
[33:30] Protein groups matrix saved to C:\Users\cajac\Documents\ProteoBench\Entrapment_Runs\DIANN_output\DIANN_search_results\report_no_Mexc_no_varmods_7_30_cutempty.pg_matrix.tsv.
[33:30] Saving gene group levels matrix
[33:30] Gene groups matrix saved to C:\Users\cajac\Documents\ProteoBench\Entrapment_Runs\DIANN_output\DIANN_search_results\report_no_Mexc_no_varmods_7_30_cutempty.gg_matrix.tsv.
[33:30] Saving unique genes levels matrix
[33:30] Unique genes matrix saved to C:\Users\cajac\Documents\ProteoBench\Entrapment_Runs\DIANN_output\DIANN_search_results\report_no_Mexc_no_varmods_7_30_cutempty.unique_genes_matrix.tsv.
[33:30] Manifest saved to C:\Users\cajac\Documents\ProteoBench\Entrapment_Runs\DIANN_output\DIANN_search_results\report_no_Mexc_no_varmods_7_30_cutempty.manifest.txt
[33:30] Stats report saved to C:\Users\cajac\Documents\ProteoBench\Entrapment_Runs\DIANN_output\DIANN_search_results\report_no_Mexc_no_varmods_7_30_cutempty.stats.tsv

