
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 13:43:33 2026
CPU: AuthenticAMD AMD Ryzen 9 5950X 16-Core Processor
SIMD instructions: AVX AVX2 FMA SSE4.1 SSE4.2 SSE4a 
Logical CPU cores: 32
111Gb 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_5_percent_no_Mexc_no_varmods_7_3_cutempty.parquet --qvalue 0.05 --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 0.05 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
WARNING: protein inference will only be performed for precursors identified with global q-value <= 0.05 or q-value <= 0.05.

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
[0:06] Library annotated with sequence database(s): C:\Users\cajac\Documents\ProteoBench\Entrapment_Runs\FDRBench_output\ProteoBenchFASTA_Entrapment_Human_with_contaminants_entrapment_pep.fasta
[0:07] Spectral library loaded: 2692562 protein isoforms, 2692562 protein groups and 8649692 precursors in 2692562 elution groups.
[0:07] Loading protein annotations from FASTA C:\Users\cajac\Documents\ProteoBench\Entrapment_Runs\FDRBench_output\ProteoBenchFASTA_Entrapment_Human_with_contaminants_entrapment_pep.fasta
[0:35] Annotating library proteins with information from the FASTA database
[0:37] Gene names missing for some isoforms
[0:37] Library contains 2692562 proteins, and 0 genes
WARNING: no gene information in the FASTA or library: consider using --ids-to-names
[0:41] Initialising library

First pass: generating a spectral library from DIA data

[0:53] File #1/3
[0:53] Loading run C:\Users\cajac\Documents\ProteoBench\Entrapment_Runs\LFQ_Astral_DIA_15min_50ng_Human_01.raw
[1:04] Pre-processing...
[1:09] 2928 MS1 and 292883 MS2 scans in 976 (inferred) and 976 (encoded) cycles, 5370648 precursors in range
[1:10] Calibrating with mass accuracies 21 (MS1), 25 (MS2)
[1:42] RT window set to 1.49073
[1:42] Peak width: 2.856
[1:42] Scan window radius set to 6
[1:42] Recommended MS1 mass accuracy setting: 2.2 ppm
[2:33] Optimised mass accuracy: 7 ppm
[2:47] Main search
[5:02] Removing low confidence identifications
[5:13] Removing interfering precursors
[5:22] Training neural networks on 162322 target and 100568 decoy PSMs
[6:47] IDs at 0.01 FDR: 81701
[6:49] Number of IDs at 0.01 FDR: 81701
[6:49] Calculating protein q-values
[6:50] Number of genes identified at 1% FDR: 0 (precursor-level), 0 (protein-level) (inference performed using proteotypic peptides only)
[6:50] Quantification
[6:52] Quantification information saved to C:\Users\cajac\Documents\ProteoBench\Entrapment_Runs\LFQ_Astral_DIA_15min_50ng_Human_01.raw.quant

[6:52] File #2/3
[6:52] Loading run C:\Users\cajac\Documents\ProteoBench\Entrapment_Runs\LFQ_Astral_DIA_15min_50ng_Human_02.raw
[7:03] Pre-processing...
[7:07] 2928 MS1 and 292914 MS2 scans in 976 (inferred) and 976 (encoded) cycles, 5370648 precursors in range
[7:08] Calibrating with mass accuracies 20 (MS1), 25 (MS2)
[7:37] RT window set to 1.56762
[7:38] Recommended MS1 mass accuracy setting: 2.3 ppm
[7:50] Main search
[9:57] Removing low confidence identifications
[10:08] Removing interfering precursors
[10:17] Training neural networks on 163437 target and 100620 decoy PSMs
[11:39] IDs at 0.01 FDR: 82781
[11:41] Number of IDs at 0.01 FDR: 82781
[11:41] Calculating protein q-values
[11:41] Number of genes identified at 1% FDR: 0 (precursor-level), 0 (protein-level) (inference performed using proteotypic peptides only)
[11:42] Quantification
[11:44] Quantification information saved to C:\Users\cajac\Documents\ProteoBench\Entrapment_Runs\LFQ_Astral_DIA_15min_50ng_Human_02.raw.quant

[11:44] File #3/3
[11:44] Loading run C:\Users\cajac\Documents\ProteoBench\Entrapment_Runs\LFQ_Astral_DIA_15min_50ng_Human_03.raw
[11:54] Pre-processing...
[11:59] 2927 MS1 and 292757 MS2 scans in 976 (inferred) and 976 (encoded) cycles, 5370648 precursors in range
[12:00] Calibrating with mass accuracies 20 (MS1), 25 (MS2)
[12:31] RT window set to 1.4467
[12:32] Recommended MS1 mass accuracy setting: 2.2 ppm
[12:45] Main search
[14:56] Removing low confidence identifications
[15:06] Removing interfering precursors
[15:15] Training neural networks on 162512 target and 100309 decoy PSMs
[16:40] IDs at 0.01 FDR: 81529
[16:42] Number of IDs at 0.01 FDR: 81529
[16:42] Calculating protein q-values
[16:42] Number of genes identified at 1% FDR: 0 (precursor-level), 0 (protein-level) (inference performed using proteotypic peptides only)
[16:42] Quantification
[16:44] Quantification information saved to C:\Users\cajac\Documents\ProteoBench\Entrapment_Runs\LFQ_Astral_DIA_15min_50ng_Human_03.raw.quant

[16:44] Cross-run analysis
[16:44] Reading quantification information: 3 files
[16:58] Quantifying peptides
[17:38] Assembling protein groups
[17:40] Quantifying proteins
[17:40] Calculating q-values for protein and gene groups
[17:41] Calculating global q-values for protein and gene groups
[17:41] Protein groups with global q-value <= 0.01: 85140
[17:42] Compressed report saved to C:\Users\cajac\Documents\ProteoBench\Entrapment_Runs\DIANN_output\DIANN_search_results\report_5_percent_no_Mexc_no_varmods_7_3_cutempty-first-pass.parquet. Use R 'arrow' or Python 'PyArrow' package to process
[17:42] Saving precursor levels matrix
[17:42] Precursor levels matrix (1% precursor and protein group FDR) saved to C:\Users\cajac\Documents\ProteoBench\Entrapment_Runs\DIANN_output\DIANN_search_results\report_5_percent_no_Mexc_no_varmods_7_3_cutempty-first-pass.pr_matrix.tsv.
[17:42] Manifest saved to C:\Users\cajac\Documents\ProteoBench\Entrapment_Runs\DIANN_output\DIANN_search_results\report_5_percent_no_Mexc_no_varmods_7_3_cutempty-first-pass.manifest.txt
[17:42] Stats report saved to C:\Users\cajac\Documents\ProteoBench\Entrapment_Runs\DIANN_output\DIANN_search_results\report_5_percent_no_Mexc_no_varmods_7_3_cutempty-first-pass.stats.tsv
[17:42] Generating spectral library:
[17:44] 112888 target and 5649 decoy precursors saved
[17:45] 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

[17:46] 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
[17:48] Spectral library loaded: 107792 protein isoforms, 107792 protein groups and 118536 precursors in 108459 elution groups.
[17:48] Loading protein annotations from FASTA C:\Users\cajac\Documents\ProteoBench\Entrapment_Runs\FDRBench_output\ProteoBenchFASTA_Entrapment_Human_with_contaminants_entrapment_pep.fasta
[18:19] Annotating library proteins with information from the FASTA database
[18:19] Gene names missing for some isoforms
[18:19] Library contains 107792 proteins, and 0 genes
WARNING: no gene information in the FASTA or library: consider using --ids-to-names
[18:19] Initialising library
[18:20] 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

[18:20] File #1/3
[18:20] Loading run C:\Users\cajac\Documents\ProteoBench\Entrapment_Runs\LFQ_Astral_DIA_15min_50ng_Human_01.raw
[18:31] Pre-processing...
[18:32] 2928 MS1 and 292883 MS2 scans in 976 (inferred) and 976 (encoded) cycles, 112888 precursors in range
[18:32] Calibrating with mass accuracies 21 (MS1), 25 (MS2)
[18:32] RT window set to 0.445531
[18:32] Recommended MS1 mass accuracy setting: 2.8 ppm
[18:33] Main search
[18:36] Removing low confidence identifications
[18:39] Removing interfering precursors
[18:40] Training neural networks on 104656 target and 52164 decoy PSMs
[19:30] IDs at 0.01 FDR: 85085
[19:31] Number of IDs at 0.01 FDR: 85085
[19:31] Calculating protein q-values
[19:31] Number of genes identified at 1% FDR: 0 (precursor-level), 0 (protein-level) (inference performed using proteotypic peptides only)
[19:31] Quantification

[19:32] File #2/3
[19:32] Loading run C:\Users\cajac\Documents\ProteoBench\Entrapment_Runs\LFQ_Astral_DIA_15min_50ng_Human_02.raw
[19:42] Pre-processing...
[19:43] 2928 MS1 and 292914 MS2 scans in 976 (inferred) and 976 (encoded) cycles, 112888 precursors in range
[19:43] Calibrating with mass accuracies 21 (MS1), 25 (MS2)
[19:44] RT window set to 0.437313
[19:44] Recommended MS1 mass accuracy setting: 2.7 ppm
[19:44] Main search
[19:47] Removing low confidence identifications
[19:50] Removing interfering precursors
[19:52] Training neural networks on 104664 target and 52137 decoy PSMs
[20:40] IDs at 0.01 FDR: 84967
[20:41] Number of IDs at 0.01 FDR: 84967
[20:41] Calculating protein q-values
[20:41] Number of genes identified at 1% FDR: 0 (precursor-level), 0 (protein-level) (inference performed using proteotypic peptides only)
[20:41] Quantification

[20:42] File #3/3
[20:42] Loading run C:\Users\cajac\Documents\ProteoBench\Entrapment_Runs\LFQ_Astral_DIA_15min_50ng_Human_03.raw
[20:52] Pre-processing...
[20:53] 2927 MS1 and 292757 MS2 scans in 976 (inferred) and 976 (encoded) cycles, 112888 precursors in range
[20:53] Calibrating with mass accuracies 20 (MS1), 25 (MS2)
[20:54] RT window set to 0.442625
[20:54] Recommended MS1 mass accuracy setting: 2.9 ppm
[20:54] Main search
[20:57] Removing low confidence identifications
[21:01] Removing interfering precursors
[21:02] Training neural networks on 103794 target and 51433 decoy PSMs
[21:51] IDs at 0.01 FDR: 84658
[21:52] Number of IDs at 0.01 FDR: 84658
[21:52] Calculating protein q-values
[21:52] Number of genes identified at 1% FDR: 0 (precursor-level), 0 (protein-level) (inference performed using proteotypic peptides only)
[21:52] Quantification

[21:53] Cross-run analysis
[21:53] Reading quantification information: 3 files
[21:54] Quantifying peptides
WARNING: QuantUMS requires 6 or more runs for the optimisation of its hyperparameters to perform best.
[23:08] Quantification parameters: 0.34523, 0.00216172, 0.00160842, 0.233804, 0.212025, 0.211332, 0.18554, 0.0691684, 0.107548, 0.125435, 0.0493509, 0.0551205, 0.509319, 0.0503265, 0.0631218, 0.0107037
[23:21] Quantifying proteins
[23:21] Calculating q-values for protein and gene groups
[23:21] Calculating global q-values for protein and gene groups
[23:21] Protein groups with global q-value <= 0.01: 84247
[23:22] Compressed report saved to C:\Users\cajac\Documents\ProteoBench\Entrapment_Runs\DIANN_output\DIANN_search_results\report_5_percent_no_Mexc_no_varmods_7_3_cutempty.parquet. Use R 'arrow' or Python 'PyArrow' package to process
[23:22] Saving precursor levels matrix
[23:23] Precursor levels matrix (1% precursor and protein group FDR) saved to C:\Users\cajac\Documents\ProteoBench\Entrapment_Runs\DIANN_output\DIANN_search_results\report_5_percent_no_Mexc_no_varmods_7_3_cutempty.pr_matrix.tsv.
[23:23] Saving protein group levels matrix
[23:23] Protein groups matrix saved to C:\Users\cajac\Documents\ProteoBench\Entrapment_Runs\DIANN_output\DIANN_search_results\report_5_percent_no_Mexc_no_varmods_7_3_cutempty.pg_matrix.tsv.
[23:23] Saving gene group levels matrix
[23:23] Gene groups matrix saved to C:\Users\cajac\Documents\ProteoBench\Entrapment_Runs\DIANN_output\DIANN_search_results\report_5_percent_no_Mexc_no_varmods_7_3_cutempty.gg_matrix.tsv.
[23:23] Saving unique genes levels matrix
[23:23] Unique genes matrix saved to C:\Users\cajac\Documents\ProteoBench\Entrapment_Runs\DIANN_output\DIANN_search_results\report_5_percent_no_Mexc_no_varmods_7_3_cutempty.unique_genes_matrix.tsv.
[23:23] Manifest saved to C:\Users\cajac\Documents\ProteoBench\Entrapment_Runs\DIANN_output\DIANN_search_results\report_5_percent_no_Mexc_no_varmods_7_3_cutempty.manifest.txt
[23:23] Stats report saved to C:\Users\cajac\Documents\ProteoBench\Entrapment_Runs\DIANN_output\DIANN_search_results\report_5_percent_no_Mexc_no_varmods_7_3_cutempty.stats.tsv

