
DIA-NN 2.3.0 Academia  (Data-Independent Acquisition by Neural Networks)
Compiled on Sep 27 2025 22:03:55
Current date and time: Thu Nov 13 13:56:03 2025
CPU: GenuineIntel Intel(R) Xeon(R) Gold 6230R CPU @ 2.10GHz
SIMD instructions: AVX AVX2 AVX512CD AVX512F FMA SSE4.1 SSE4.2 
Logical CPU cores: 104
diann.exe --f \\tol-brandir\Masse\Public datasets\ProteoBench\ASTRAL_DIA_BART\LFQ_Astral_DIA_15min_50ng_Condition_A_REP1.raw  --f \\tol-brandir\Masse\Public datasets\ProteoBench\ASTRAL_DIA_BART\LFQ_Astral_DIA_15min_50ng_Condition_A_REP2.raw  --f \\tol-brandir\Masse\Public datasets\ProteoBench\ASTRAL_DIA_BART\LFQ_Astral_DIA_15min_50ng_Condition_A_REP3.raw  --f \\tol-brandir\Masse\Public datasets\ProteoBench\ASTRAL_DIA_BART\LFQ_Astral_DIA_15min_50ng_Condition_B_REP1.raw  --f \\tol-brandir\Masse\Public datasets\ProteoBench\ASTRAL_DIA_BART\LFQ_Astral_DIA_15min_50ng_Condition_B_REP2.raw  --f \\tol-brandir\Masse\Public datasets\ProteoBench\ASTRAL_DIA_BART\LFQ_Astral_DIA_15min_50ng_Condition_B_REP3.raw  --lib  --threads 52 --verbose 1 --out D:\Utilisateurs\chaoui\diaNN results\ProteoBench Astral\diaNN2.3\proteotypicity ProteinNames\report.parquet --qvalue 0.01 --matrices --out-lib D:\Utilisateurs\chaoui\diaNN results\ProteoBench Astral\diaNN2.3\proteotypicity ProteinNames\report-lib.parquet --gen-spec-lib --predictor --fasta C:\DIA-NN\Fasta\ProteoBenchFASTA_DDAQuantification.fasta --fasta-search --met-excision --min-pep-len 7 --max-pep-len 30 --min-pr-mz 380 --max-pr-mz 980 --min-pr-charge 2 --max-pr-charge 4 --min-fr-mz 150 --max-fr-mz 2000 --cut K*,R* --missed-cleavages 1 --unimod4 --var-mods 1 --var-mod UniMod:35,15.994915,M --peptidoforms --reanalyse --rt-profiling --pg-level 1 

Thread number set to 52
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
Deep learning will be used to generate a new in silico spectral library from peptides provided
DIA-NN will carry out FASTA digest for in silico lib generation
N-terminal methionine excision enabled
Min peptide length set to 7
Max peptide length set to 30
Min precursor m/z set to 380
Max precursor m/z set to 980
Min precursor charge set to 2
Max precursor charge set to 4
Min fragment m/z set to 150
Max fragment m/z set to 2000
In silico digest will involve cuts at K*,R*
Maximum number of missed cleavages set to 1
Cysteine carbamidomethylation enabled as a fixed modification
Maximum number of variable modifications set to 1
Modification UniMod:35 with mass delta 15.9949 at M will be considered as variable
Peptidoform scoring enabled
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
Implicit protein grouping: protein names; this determines which peptides are considered 'proteotypic' and thus affects protein FDR calculation
DIA-NN will automatically optimise the mass accuracy for the first run of the experiment, use this mode for preliminary analyses only
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
The following variable modifications will be localised: UniMod:35 

6 files will be processed
[0:00] Loading FASTA C:\DIA-NN\Fasta\ProteoBenchFASTA_DDAQuantification.fasta
[0:04] Processing FASTA
[0:08] Assembling elution groups
[0:14] 4547529 precursors generated
[0:14] Protein names missing for some isoforms
[0:14] Gene names missing for some isoforms
[0:14] Library contains 31678 proteins, and 0 genes
[0:21] [0:34] [14:05] [16:13] [16:16] [16:17] Saving the library to D:\Utilisateurs\chaoui\diaNN results\ProteoBench Astral\diaNN2.3\proteotypicity ProteinNames\report-lib.predicted.speclib
[16:24] Initialising library
[16:41] Loading spectral library D:\Utilisateurs\chaoui\diaNN results\ProteoBench Astral\diaNN2.3\proteotypicity ProteinNames\report-lib.predicted.speclib
[16:45] Library annotated with sequence database(s): C:\DIA-NN\Fasta\ProteoBenchFASTA_DDAQuantification.fasta
[16:46] Spectral library loaded: 31829 protein isoforms, 42030 protein groups and 4547529 precursors in 2428292 elution groups.
[16:46] Loading protein annotations from FASTA C:\DIA-NN\Fasta\ProteoBenchFASTA_DDAQuantification.fasta
[16:46] Annotating library proteins with information from the FASTA database
[16:46] Protein names missing for some isoforms
[16:46] Gene names missing for some isoforms
[16:46] Library contains 31678 proteins, and 0 genes
[16:50] Initialising library

First pass: generating a spectral library from DIA data

[17:05] File #1/6
[17:05] Loading run \\tol-brandir\Masse\Public datasets\ProteoBench\ASTRAL_DIA_BART\LFQ_Astral_DIA_15min_50ng_Condition_A_REP1.raw
[18:03] Pre-processing...
[18:05] 2931 MS1 and 293271 MS2 scans in 977 (inferred) and 977 (encoded) cycles, 4543889 precursors in range
[18:06] Calibrating with mass accuracies 21 (MS1), 25 (MS2)
[18:29] RT window set to 1.16595
[18:29] Peak width: 2.88
[18:29] Scan window radius set to 6
[18:29] Recommended MS1 mass accuracy setting: 2.5 ppm
[18:49] Optimised mass accuracy: 6 ppm
[18:55] Searching decoys
[19:28] Main search
[20:33] Removing low confidence identifications
[20:50] Removing interfering precursors
[21:01] Training neural networks on 212197 target and 131707 decoy PSMs
[22:53] Training neural networks on 212197 target and 132334 decoy PSMs
[24:39] IDs at 0.01 FDR: 103109
[24:40] Precursors at 1% peptidoform FDR: 101062
[24:42] Number of IDs at 0.01 FDR: 109307
[24:42] Calculating protein q-values
[24:43] Number of proteins identified at 1% FDR: 11068 (precursor-level), 10059 (protein-level) (inference performed using proteotypic peptides only)
[24:43] Quantification
[24:44] Precursors with scored PTMs at 1% FDR: 2361 out of 2675 considered
[24:44] Precursors with all scored PTM sites unoccupied at 1% FDR: 101147
[24:44] Precursors with PTMs localised (when required) with > 90% confidence: 2260 out of 2361
[24:52] Quantification information saved to \\tol-brandir\Masse\Public datasets\ProteoBench\ASTRAL_DIA_BART\LFQ_Astral_DIA_15min_50ng_Condition_A_REP1.raw.quant

[24:52] File #2/6
[24:52] Loading run \\tol-brandir\Masse\Public datasets\ProteoBench\ASTRAL_DIA_BART\LFQ_Astral_DIA_15min_50ng_Condition_A_REP2.raw
[25:47] Pre-processing...
[25:49] 2933 MS1 and 293433 MS2 scans in 978 (inferred) and 978 (encoded) cycles, 4543889 precursors in range
[25:50] Calibrating with mass accuracies 21 (MS1), 25 (MS2)
[26:13] RT window set to 1.31087
[26:13] Recommended MS1 mass accuracy setting: 2.7 ppm
[26:19] Searching decoys
[26:53] Main search
[27:59] Removing low confidence identifications
[28:15] Removing interfering precursors
[28:26] Training neural networks on 216406 target and 132870 decoy PSMs
[30:15] Training neural networks on 216406 target and 134390 decoy PSMs
[31:56] IDs at 0.01 FDR: 106345
[31:57] Precursors at 1% peptidoform FDR: 103169
[31:59] Number of IDs at 0.01 FDR: 112731
[31:59] Calculating protein q-values
[32:00] Number of proteins identified at 1% FDR: 11190 (precursor-level), 10135 (protein-level) (inference performed using proteotypic peptides only)
[32:00] Quantification
[32:01] Precursors with scored PTMs at 1% FDR: 2375 out of 2873 considered
[32:01] Precursors with all scored PTM sites unoccupied at 1% FDR: 102561
[32:01] Precursors with PTMs localised (when required) with > 90% confidence: 2292 out of 2375
[32:09] Quantification information saved to \\tol-brandir\Masse\Public datasets\ProteoBench\ASTRAL_DIA_BART\LFQ_Astral_DIA_15min_50ng_Condition_A_REP2.raw.quant

[32:09] File #3/6
[32:09] Loading run \\tol-brandir\Masse\Public datasets\ProteoBench\ASTRAL_DIA_BART\LFQ_Astral_DIA_15min_50ng_Condition_A_REP3.raw
[33:09] Pre-processing...
[33:11] 2932 MS1 and 293358 MS2 scans in 978 (inferred) and 978 (encoded) cycles, 4543889 precursors in range
[33:12] Calibrating with mass accuracies 21 (MS1), 25 (MS2)
[33:36] RT window set to 1.26061
[33:36] Recommended MS1 mass accuracy setting: 2.5 ppm
[33:42] Searching decoys
[34:15] Main search
[35:18] Removing low confidence identifications
[35:34] Removing interfering precursors
[35:45] Training neural networks on 217916 target and 133928 decoy PSMs
[37:38] Training neural networks on 217916 target and 135085 decoy PSMs
[39:29] IDs at 0.01 FDR: 106235
[39:30] Precursors at 1% peptidoform FDR: 103123
[39:32] Number of IDs at 0.01 FDR: 112487
[39:32] Calculating protein q-values
[39:32] Number of proteins identified at 1% FDR: 11199 (precursor-level), 10081 (protein-level) (inference performed using proteotypic peptides only)
[39:33] Quantification
[39:34] Precursors with scored PTMs at 1% FDR: 2270 out of 2826 considered
[39:34] Precursors with all scored PTM sites unoccupied at 1% FDR: 102770
[39:34] Precursors with PTMs localised (when required) with > 90% confidence: 2193 out of 2270
[39:42] Quantification information saved to \\tol-brandir\Masse\Public datasets\ProteoBench\ASTRAL_DIA_BART\LFQ_Astral_DIA_15min_50ng_Condition_A_REP3.raw.quant

[39:42] File #4/6
[39:42] Loading run \\tol-brandir\Masse\Public datasets\ProteoBench\ASTRAL_DIA_BART\LFQ_Astral_DIA_15min_50ng_Condition_B_REP1.raw
[40:41] Pre-processing...
[40:43] 2933 MS1 and 293382 MS2 scans in 978 (inferred) and 978 (encoded) cycles, 4543889 precursors in range
[40:43] Calibrating with mass accuracies 21 (MS1), 25 (MS2)
[41:07] RT window set to 1.16977
[41:07] Recommended MS1 mass accuracy setting: 2.7 ppm
[41:13] Searching decoys
[41:44] Main search
[42:45] Removing low confidence identifications
[43:01] Removing interfering precursors
[43:12] Training neural networks on 223512 target and 138940 decoy PSMs
[45:08] Training neural networks on 223512 target and 140322 decoy PSMs
[46:55] IDs at 0.01 FDR: 106183
[46:56] Precursors at 1% peptidoform FDR: 103452
[46:58] Number of IDs at 0.01 FDR: 113075
[46:58] Calculating protein q-values
[46:59] Number of proteins identified at 1% FDR: 10949 (precursor-level), 9855 (protein-level) (inference performed using proteotypic peptides only)
[46:59] Quantification
[47:01] Precursors with scored PTMs at 1% FDR: 2997 out of 3494 considered
[47:01] Precursors with all scored PTM sites unoccupied at 1% FDR: 102707
[47:01] Precursors with PTMs localised (when required) with > 90% confidence: 2879 out of 2997
[47:07] Quantification information saved to \\tol-brandir\Masse\Public datasets\ProteoBench\ASTRAL_DIA_BART\LFQ_Astral_DIA_15min_50ng_Condition_B_REP1.raw.quant

[47:07] File #5/6
[47:07] Loading run \\tol-brandir\Masse\Public datasets\ProteoBench\ASTRAL_DIA_BART\LFQ_Astral_DIA_15min_50ng_Condition_B_REP2.raw
[48:05] Pre-processing...
[48:07] 2933 MS1 and 293330 MS2 scans in 978 (inferred) and 978 (encoded) cycles, 4543889 precursors in range
[48:08] Calibrating with mass accuracies 21 (MS1), 25 (MS2)
[48:32] RT window set to 1.13845
[48:32] Recommended MS1 mass accuracy setting: 2.9 ppm
[48:37] Searching decoys
[49:08] Main search
[50:06] Removing low confidence identifications
[50:23] Removing interfering precursors
[50:34] Training neural networks on 218056 target and 135307 decoy PSMs
[52:25] Training neural networks on 218056 target and 136043 decoy PSMs
[54:08] IDs at 0.01 FDR: 105654
[54:08] Precursors at 1% peptidoform FDR: 103163
[54:10] Number of IDs at 0.01 FDR: 113246
[54:10] Calculating protein q-values
[54:11] Number of proteins identified at 1% FDR: 10985 (precursor-level), 9898 (protein-level) (inference performed using proteotypic peptides only)
[54:11] Quantification
[54:13] Precursors with scored PTMs at 1% FDR: 2986 out of 3509 considered
[54:13] Precursors with all scored PTM sites unoccupied at 1% FDR: 102544
[54:13] Precursors with PTMs localised (when required) with > 90% confidence: 2882 out of 2986
[54:20] Quantification information saved to \\tol-brandir\Masse\Public datasets\ProteoBench\ASTRAL_DIA_BART\LFQ_Astral_DIA_15min_50ng_Condition_B_REP2.raw.quant

[54:20] File #6/6
[54:20] Loading run \\tol-brandir\Masse\Public datasets\ProteoBench\ASTRAL_DIA_BART\LFQ_Astral_DIA_15min_50ng_Condition_B_REP3.raw
[55:18] Pre-processing...
[55:20] 2934 MS1 and 293446 MS2 scans in 978 (inferred) and 978 (encoded) cycles, 4543889 precursors in range
[55:21] Calibrating with mass accuracies 21 (MS1), 25 (MS2)
[55:44] RT window set to 1.36265
[55:44] Recommended MS1 mass accuracy setting: 2.5 ppm
[55:51] Searching decoys
[56:27] Main search
[57:38] Removing low confidence identifications
[57:55] Removing interfering precursors
[58:07] Training neural networks on 215448 target and 132435 decoy PSMs
[60:03] Training neural networks on 215448 target and 133595 decoy PSMs
[61:55] IDs at 0.01 FDR: 106704
[61:56] Precursors at 1% peptidoform FDR: 103880
[61:58] Number of IDs at 0.01 FDR: 113185
[61:58] Calculating protein q-values
[61:58] Number of proteins identified at 1% FDR: 11050 (precursor-level), 9897 (protein-level) (inference performed using proteotypic peptides only)
[61:58] Quantification
[62:00] Precursors with scored PTMs at 1% FDR: 2953 out of 3418 considered
[62:00] Precursors with all scored PTM sites unoccupied at 1% FDR: 102976
[62:00] Precursors with PTMs localised (when required) with > 90% confidence: 2847 out of 2953
[62:07] Quantification information saved to \\tol-brandir\Masse\Public datasets\ProteoBench\ASTRAL_DIA_BART\LFQ_Astral_DIA_15min_50ng_Condition_B_REP3.raw.quant

[62:07] Cross-run analysis
[62:07] Reading quantification information: 6 files
[62:29] Quantifying peptides
[63:12] Assembling protein groups
[63:14] Quantifying proteins
[63:15] Calculating q-values for protein and gene groups
[63:17] Calculating global q-values for protein and gene groups
[63:18] Protein groups with global q-value <= 0.01: 11576
[63:23] Compressed report saved to D:\Utilisateurs\chaoui\diaNN results\ProteoBench Astral\diaNN2.3\proteotypicity ProteinNames\report-first-pass.parquet. Use R 'arrow' or Python 'PyArrow' package to process
[63:23] Site report saved to D:\Utilisateurs\chaoui\diaNN results\ProteoBench Astral\diaNN2.3\proteotypicity ProteinNames\report-first-pass.site_report.parquet
[63:23] Saving precursor levels matrix
[63:24] Precursor levels matrix (1% precursor and protein group FDR) saved to D:\Utilisateurs\chaoui\diaNN results\ProteoBench Astral\diaNN2.3\proteotypicity ProteinNames\report-first-pass.pr_matrix.tsv.
[63:24] Manifest saved to D:\Utilisateurs\chaoui\diaNN results\ProteoBench Astral\diaNN2.3\proteotypicity ProteinNames\report-first-pass.manifest.txt
[63:24] Stats report saved to D:\Utilisateurs\chaoui\diaNN results\ProteoBench Astral\diaNN2.3\proteotypicity ProteinNames\report-first-pass.stats.tsv
[63:24] Generating spectral library:
[63:28] 139135 target and 1420 decoy precursors saved
[63:28] Spectral library saved to D:\Utilisateurs\chaoui\diaNN results\ProteoBench Astral\diaNN2.3\proteotypicity ProteinNames\report-lib.parquet

[63:29] Loading spectral library D:\Utilisateurs\chaoui\diaNN results\ProteoBench Astral\diaNN2.3\proteotypicity ProteinNames\report-lib.parquet
[63:31] Spectral library loaded: 12991 protein isoforms, 12857 protein groups and 140555 precursors in 131440 elution groups.
[63:31] Loading protein annotations from FASTA C:\DIA-NN\Fasta\ProteoBenchFASTA_DDAQuantification.fasta
[63:31] Annotating library proteins with information from the FASTA database
[63:31] Gene names missing for some isoforms
[63:31] Library contains 12981 proteins, and 0 genes
[63:31] Initialising library
[63:35] Saving the library to D:\Utilisateurs\chaoui\diaNN results\ProteoBench Astral\diaNN2.3\proteotypicity ProteinNames\report-lib.parquet.skyline.speclib


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

[63:36] File #1/6
[63:36] Loading run \\tol-brandir\Masse\Public datasets\ProteoBench\ASTRAL_DIA_BART\LFQ_Astral_DIA_15min_50ng_Condition_A_REP1.raw
[63:57] Pre-processing...
[63:58] 2931 MS1 and 293271 MS2 scans in 977 (inferred) and 977 (encoded) cycles, 139135 precursors in range
[63:58] Calibrating with mass accuracies 21 (MS1), 25 (MS2)
[63:58] RT window set to 0.441913
[63:58] Recommended MS1 mass accuracy setting: 3.7 ppm
[63:59] Searching decoys
[63:59] Main search
[64:02] Removing low confidence identifications
[64:08] Removing interfering precursors
[64:10] Training neural networks on 121906 target and 57605 decoy PSMs
[65:05] Training neural networks on 121837 target and 67100 decoy PSMs
[66:02] IDs at 0.01 FDR: 117223
[66:02] Precursors at 1% peptidoform FDR: 115010
[66:02] Number of IDs at 0.01 FDR: 120016
[66:02] Calculating protein q-values
[66:02] Number of proteins identified at 1% FDR: 11026 (precursor-level), 10526 (protein-level) (inference performed using proteotypic peptides only)
[66:02] Quantification
[66:04] Precursors with scored PTMs at 1% FDR: 2856 out of 2958 considered
[66:04] Precursors with all scored PTM sites unoccupied at 1% FDR: 113696
[66:04] Precursors with PTMs localised (when required) with > 90% confidence: 2749 out of 2856

[66:05] File #2/6
[66:05] Loading run \\tol-brandir\Masse\Public datasets\ProteoBench\ASTRAL_DIA_BART\LFQ_Astral_DIA_15min_50ng_Condition_A_REP2.raw
[66:26] Pre-processing...
[66:27] 2933 MS1 and 293433 MS2 scans in 978 (inferred) and 978 (encoded) cycles, 139135 precursors in range
[66:27] Calibrating with mass accuracies 21 (MS1), 25 (MS2)
[66:28] RT window set to 0.442342
[66:28] Recommended MS1 mass accuracy setting: 3.1 ppm
[66:29] Searching decoys
[66:29] Main search
[66:32] Removing low confidence identifications
[66:37] Removing interfering precursors
[66:39] Training neural networks on 122396 target and 57990 decoy PSMs
[67:35] Training neural networks on 122342 target and 67593 decoy PSMs
[68:33] IDs at 0.01 FDR: 117547
[68:34] Precursors at 1% peptidoform FDR: 115883
[68:34] Number of IDs at 0.01 FDR: 120887
[68:34] Calculating protein q-values
[68:34] Number of proteins identified at 1% FDR: 11001 (precursor-level), 10512 (protein-level) (inference performed using proteotypic peptides only)
[68:34] Quantification
[68:35] Precursors with scored PTMs at 1% FDR: 2943 out of 3032 considered
[68:35] Precursors with all scored PTM sites unoccupied at 1% FDR: 114862
[68:35] Precursors with PTMs localised (when required) with > 90% confidence: 2840 out of 2943

[68:36] File #3/6
[68:36] Loading run \\tol-brandir\Masse\Public datasets\ProteoBench\ASTRAL_DIA_BART\LFQ_Astral_DIA_15min_50ng_Condition_A_REP3.raw
[68:57] Pre-processing...
[68:59] 2932 MS1 and 293358 MS2 scans in 978 (inferred) and 978 (encoded) cycles, 139135 precursors in range
[68:59] Calibrating with mass accuracies 21 (MS1), 25 (MS2)
[68:59] RT window set to 0.430871
[68:59] Recommended MS1 mass accuracy setting: 3.2 ppm
[69:00] Searching decoys
[69:00] Main search
[69:03] Removing low confidence identifications
[69:08] Removing interfering precursors
[69:10] Training neural networks on 122292 target and 57876 decoy PSMs
[70:06] Training neural networks on 122231 target and 67445 decoy PSMs
[71:02] IDs at 0.01 FDR: 117956
[71:02] Precursors at 1% peptidoform FDR: 116281
[71:02] Number of IDs at 0.01 FDR: 120860
[71:02] Calculating protein q-values
[71:03] Number of proteins identified at 1% FDR: 11012 (precursor-level), 10503 (protein-level) (inference performed using proteotypic peptides only)
[71:03] Quantification
[71:04] Precursors with scored PTMs at 1% FDR: 2932 out of 3015 considered
[71:04] Precursors with all scored PTM sites unoccupied at 1% FDR: 115040
[71:04] Precursors with PTMs localised (when required) with > 90% confidence: 2830 out of 2932

[71:05] File #4/6
[71:05] Loading run \\tol-brandir\Masse\Public datasets\ProteoBench\ASTRAL_DIA_BART\LFQ_Astral_DIA_15min_50ng_Condition_B_REP1.raw
[71:26] Pre-processing...
[71:28] 2933 MS1 and 293382 MS2 scans in 978 (inferred) and 978 (encoded) cycles, 139135 precursors in range
[71:28] Calibrating with mass accuracies 22 (MS1), 25 (MS2)
[71:28] RT window set to 0.455967
[71:29] Recommended MS1 mass accuracy setting: 3.5 ppm
[71:29] Searching decoys
[71:30] Main search
[71:33] Removing low confidence identifications
[71:38] Removing interfering precursors
[71:40] Training neural networks on 123136 target and 59057 decoy PSMs
[72:33] Training neural networks on 123073 target and 68207 decoy PSMs
[73:29] IDs at 0.01 FDR: 118486
[73:30] Precursors at 1% peptidoform FDR: 116462
[73:30] Number of IDs at 0.01 FDR: 121504
[73:30] Calculating protein q-values
[73:30] Number of proteins identified at 1% FDR: 10975 (precursor-level), 10531 (protein-level) (inference performed using proteotypic peptides only)
[73:30] Quantification
[73:31] Precursors with scored PTMs at 1% FDR: 3157 out of 3237 considered
[73:31] Precursors with all scored PTM sites unoccupied at 1% FDR: 115015
[73:31] Precursors with PTMs localised (when required) with > 90% confidence: 3053 out of 3157

[73:32] File #5/6
[73:32] Loading run \\tol-brandir\Masse\Public datasets\ProteoBench\ASTRAL_DIA_BART\LFQ_Astral_DIA_15min_50ng_Condition_B_REP2.raw
[73:54] Pre-processing...
[73:55] 2933 MS1 and 293330 MS2 scans in 978 (inferred) and 978 (encoded) cycles, 139135 precursors in range
[73:55] Calibrating with mass accuracies 21 (MS1), 25 (MS2)
[73:56] RT window set to 0.454082
[73:56] Recommended MS1 mass accuracy setting: 3.1 ppm
[73:56] Searching decoys
[73:57] Main search
[73:59] Removing low confidence identifications
[74:05] Removing interfering precursors
[74:07] Training neural networks on 123141 target and 58798 decoy PSMs
[75:03] Training neural networks on 123085 target and 68419 decoy PSMs
[76:01] IDs at 0.01 FDR: 119011
[76:01] Precursors at 1% peptidoform FDR: 116800
[76:01] Number of IDs at 0.01 FDR: 121392
[76:01] Calculating protein q-values
[76:01] Number of proteins identified at 1% FDR: 11059 (precursor-level), 10563 (protein-level) (inference performed using proteotypic peptides only)
[76:01] Quantification
[76:03] Precursors with scored PTMs at 1% FDR: 3179 out of 3273 considered
[76:03] Precursors with all scored PTM sites unoccupied at 1% FDR: 114883
[76:03] Precursors with PTMs localised (when required) with > 90% confidence: 3084 out of 3179

[76:04] File #6/6
[76:04] Loading run \\tol-brandir\Masse\Public datasets\ProteoBench\ASTRAL_DIA_BART\LFQ_Astral_DIA_15min_50ng_Condition_B_REP3.raw
[76:26] Pre-processing...
[76:27] 2934 MS1 and 293446 MS2 scans in 978 (inferred) and 978 (encoded) cycles, 139135 precursors in range
[76:27] Calibrating with mass accuracies 21 (MS1), 25 (MS2)
[76:27] RT window set to 0.45243
[76:27] Recommended MS1 mass accuracy setting: 3 ppm
[76:28] Searching decoys
[76:29] Main search
[76:31] Removing low confidence identifications
[76:36] Removing interfering precursors
[76:39] Training neural networks on 123090 target and 58456 decoy PSMs
[77:29] Training neural networks on 123022 target and 68290 decoy PSMs
[78:24] IDs at 0.01 FDR: 118621
[78:24] Precursors at 1% peptidoform FDR: 116544
[78:24] Number of IDs at 0.01 FDR: 121510
[78:24] Calculating protein q-values
[78:24] Number of proteins identified at 1% FDR: 11046 (precursor-level), 10548 (protein-level) (inference performed using proteotypic peptides only)
[78:24] Quantification
[78:26] Precursors with scored PTMs at 1% FDR: 3171 out of 3257 considered
[78:26] Precursors with all scored PTM sites unoccupied at 1% FDR: 115003
[78:26] Precursors with PTMs localised (when required) with > 90% confidence: 3075 out of 3171

[78:27] Cross-run analysis
[78:27] Reading quantification information: 6 files
[78:31] Quantifying peptides
[80:51] Quantification parameters: 0.36893, 0.00135794, 0.0016246, 0.012294, 0.0120621, 0.0119888, 0.180976, 0.24947, 0.195754, 0.0134503, 0.0353203, 0.0145051, 0.379646, 0.0529472, 0.0771793, 0.0116791
[81:09] Quantifying proteins
[81:10] Calculating q-values for protein and gene groups
[81:10] Calculating global q-values for protein and gene groups
[81:10] Protein groups with global q-value <= 0.01: 11067
[81:16] Compressed report saved to D:\Utilisateurs\chaoui\diaNN results\ProteoBench Astral\diaNN2.3\proteotypicity ProteinNames\report.parquet. Use R 'arrow' or Python 'PyArrow' package to process
[81:16] Site report saved to D:\Utilisateurs\chaoui\diaNN results\ProteoBench Astral\diaNN2.3\proteotypicity ProteinNames\report.site_report.parquet
[81:16] Saving precursor levels matrix
[81:16] Precursor levels matrix (1% precursor and protein group FDR) saved to D:\Utilisateurs\chaoui\diaNN results\ProteoBench Astral\diaNN2.3\proteotypicity ProteinNames\report.pr_matrix.tsv.
[81:16] Saving protein group levels matrix
ERROR: cannot write to D:\Utilisateurs\chaoui\diaNN results\ProteoBench Astral\diaNN2.3\proteotypicity ProteinNames\report.pg_matrix.tsv. Check if the destination folder is write-protected or the file is in use
[81:16] Saving gene group levels matrix
[81:16] Gene groups matrix saved to D:\Utilisateurs\chaoui\diaNN results\ProteoBench Astral\diaNN2.3\proteotypicity ProteinNames\report.gg_matrix.tsv.
[81:16] Saving unique genes levels matrix
[81:16] Unique genes matrix saved to D:\Utilisateurs\chaoui\diaNN results\ProteoBench Astral\diaNN2.3\proteotypicity ProteinNames\report.unique_genes_matrix.tsv.
[81:17] Manifest saved to D:\Utilisateurs\chaoui\diaNN results\ProteoBench Astral\diaNN2.3\proteotypicity ProteinNames\report.manifest.txt
[81:17] Stats report saved to D:\Utilisateurs\chaoui\diaNN results\ProteoBench Astral\diaNN2.3\proteotypicity ProteinNames\report.stats.tsv

