
DIA-NN 2.3.2 Academia  (Data-Independent Acquisition by Neural Networks)
Compiled on Jan 22 2026 05:01:57
Current date and time: Thu Jul 23 18:58:05 2026
Logical CPU cores: 128
/opt/diann-2.3.2/diann-linux --lib out-DIANN_libA/WU2.3.2_metox_report-lib.predicted.speclib --fasta input/ProteoBenchFASTA_Entrapment_Human_with_contaminants_entrapment_pep.fasta --reannotate --f /raw/LFQ_Astral_DIA_15min_50ng_Condition_A_REP1.raw --f /raw/LFQ_Astral_DIA_15min_50ng_Condition_A_REP2.raw --f /raw/LFQ_Astral_DIA_15min_50ng_Condition_A_REP3.raw --f /raw/LFQ_Astral_DIA_15min_50ng_Condition_B_REP1.raw --f /raw/LFQ_Astral_DIA_15min_50ng_Condition_B_REP2.raw --f /raw/LFQ_Astral_DIA_15min_50ng_Condition_B_REP3.raw --threads 24 --qvalue 0.01 --cut  --min-pep-len 7 --max-pep-len 30 --min-pr-charge 2 --max-pr-charge 4 --min-pr-mz 380 --max-pr-mz 980 --min-fr-mz 200 --max-fr-mz 1800 --missed-cleavages 0 --verbose 1 --var-mods 1 --var-mod UniMod:35,15.994915,M --met-excision --unimod4 --rt-profiling --matrices --pg-level 1 --reanalyse --gen-spec-lib --out-lib out-DIANN_quantB/WU2.3.2_metox_report-lib.parquet --out out-DIANN_quantB/WU2.3.2_metox_report.parquet --temp temp-DIANN_quantB 

Library precursors will be reannotated using the FASTA database
Thread number set to 24
Output will be filtered at 0.01 FDR
Min peptide length set to 7
Max peptide length set to 30
Min precursor charge set to 2
Max precursor charge set to 4
Min precursor m/z set to 380
Max precursor m/z set to 980
Min fragment m/z set to 200
Max fragment m/z set to 1800
Maximum number of missed cleavages set to 0
Maximum number of variable modifications set to 1
Modification UniMod:35 with mass delta 15.9949 at M will be considered as variable
N-terminal methionine excision enabled
Cysteine carbamidomethylation enabled as a fixed modification
The spectral library (if generated) will retain the original spectra but will include empirically-aligned RTs
Precursor/protein x samples expression level matrices will be saved along with the main report
Implicit protein grouping: protein names; this determines which peptides are considered 'proteotypic' and thus affects protein FDR calculation
MBR enabled; .quant files will only be saved to disk during the first pass
A spectral library will be generated
DIA-NN will automatically optimise the mass accuracy for the first run of the experiment, use this mode for preliminary analyses only
WARNING: peptidoform scoring enabled because variable modifications have been declared; to disable, use --no-peptidoforms
The following variable modifications will be localised: UniMod:35 

6 files will be processed
[0:00] Loading spectral library out-DIANN_libA/WU2.3.2_metox_report-lib.predicted.speclib
[0:05] Library annotated with sequence database(s): input/ProteoBenchFASTA_Entrapment_Human_with_contaminants_entrapment_pep.fasta
[0:07] Spectral library loaded: 2655626 protein isoforms, 2655543 protein groups and 6661787 precursors in 3554877 elution groups.
[0:07] Loading FASTA input/ProteoBenchFASTA_Entrapment_Human_with_contaminants_entrapment_pep.fasta
[2:35] Reannotating library precursors with information from the FASTA database
[3:11] Finding proteotypic peptides (assuming that the list of UniProt ids provided for each peptide is complete)
[3:12] 6661787 precursors generated
[3:17] Gene names missing for some isoforms
[3:17] Library contains 2655626 proteins, and 0 genes
WARNING: no gene information in the FASTA or library: consider using --ids-to-names
[3:27] Initialising library

First pass: generating a spectral library from DIA data

[4:01] File #1/6
[4:01] Loading run /raw/LFQ_Astral_DIA_15min_50ng_Condition_A_REP1.raw
[4:19] Pre-processing...
[4:21] 2931 MS1 and 293271 MS2 scans in 977 (inferred) and 977 (encoded) cycles, 6656386 precursors in range
[4:21] Calibrating with mass accuracies 21 (MS1), 25 (MS2)
[5:03] RT window set to 1.49544
[5:03] Peak width: 2.824
[5:03] Scan window radius set to 6
[5:03] Recommended MS1 mass accuracy setting: 2.7 ppm
[5:47] Optimised mass accuracy: 8 ppm
[6:07] Searching decoys
[7:15] Main search
[9:07] Removing low confidence identifications
[9:32] Removing interfering precursors
[9:50] Training neural networks on 287486 target and 254504 decoy PSMs
[11:18] Training neural networks on 287486 target and 252657 decoy PSMs
[13:02] IDs at 0.01 FDR: 66674
[13:03] Precursors at 1% peptidoform FDR: 65352
[13:05] Number of IDs at 0.01 FDR: 66678
[13:05] Calculating protein q-values
[13:06] Number of proteins identified at 1% FDR: 60382 (precursor-level), 59841 (protein-level) (inference performed using proteotypic peptides only)
[13:06] Quantification
[13:07] Precursors with scored PTMs at 1% FDR: 1631 out of 1731 considered
[13:07] Precursors with all scored PTM sites unoccupied at 1% FDR: 63716
[13:07] Precursors with PTMs localised (when required) with > 90% confidence: 1580 out of 1631
[13:08] Quantification information saved to temp-DIANN_quantB/_raw_LFQ_Astral_DIA_15min_50ng_Condition_A_REP1_raw.quant

[13:09] File #2/6
[13:09] Loading run /raw/LFQ_Astral_DIA_15min_50ng_Condition_A_REP2.raw
[13:24] Pre-processing...
[13:25] 2933 MS1 and 293433 MS2 scans in 978 (inferred) and 978 (encoded) cycles, 6656386 precursors in range
[13:26] Calibrating with mass accuracies 21 (MS1), 25 (MS2)
[14:03] RT window set to 1.53602
[14:03] Recommended MS1 mass accuracy setting: 2.5 ppm
[14:16] Searching decoys
[15:09] Main search
[16:57] Removing low confidence identifications
[17:26] Removing interfering precursors
[17:50] Training neural networks on 283457 target and 245848 decoy PSMs
[19:32] Training neural networks on 283457 target and 244892 decoy PSMs
[21:14] IDs at 0.01 FDR: 68749
[21:14] Precursors at 1% peptidoform FDR: 66954
[21:16] Number of IDs at 0.01 FDR: 68755
[21:16] Calculating protein q-values
[21:17] Number of proteins identified at 1% FDR: 62091 (precursor-level), 61444 (protein-level) (inference performed using proteotypic peptides only)
[21:17] Quantification
[21:18] Precursors with scored PTMs at 1% FDR: 1666 out of 1820 considered
[21:18] Precursors with all scored PTM sites unoccupied at 1% FDR: 65283
[21:18] Precursors with PTMs localised (when required) with > 90% confidence: 1610 out of 1666
[21:19] Quantification information saved to temp-DIANN_quantB/_raw_LFQ_Astral_DIA_15min_50ng_Condition_A_REP2_raw.quant

[21:19] File #3/6
[21:19] Loading run /raw/LFQ_Astral_DIA_15min_50ng_Condition_A_REP3.raw
[21:34] Pre-processing...
[21:36] 2932 MS1 and 293358 MS2 scans in 978 (inferred) and 978 (encoded) cycles, 6656386 precursors in range
[21:36] Calibrating with mass accuracies 21 (MS1), 25 (MS2)
[22:16] RT window set to 1.45569
[22:16] Recommended MS1 mass accuracy setting: 2.5 ppm
[22:35] Searching decoys
[23:42] Main search
[25:30] Removing low confidence identifications
[25:54] Removing interfering precursors
[26:11] Training neural networks on 288520 target and 250688 decoy PSMs
[27:50] Training neural networks on 288520 target and 249707 decoy PSMs
[29:48] IDs at 0.01 FDR: 68778
[29:49] Precursors at 1% peptidoform FDR: 67099
[29:51] Number of IDs at 0.01 FDR: 68779
[29:51] Calculating protein q-values
[29:52] Number of proteins identified at 1% FDR: 62118 (precursor-level), 61385 (protein-level) (inference performed using proteotypic peptides only)
[29:52] Quantification
[29:54] Precursors with scored PTMs at 1% FDR: 1705 out of 1848 considered
[29:54] Precursors with all scored PTM sites unoccupied at 1% FDR: 65394
[29:54] Precursors with PTMs localised (when required) with > 90% confidence: 1642 out of 1705
[29:55] Quantification information saved to temp-DIANN_quantB/_raw_LFQ_Astral_DIA_15min_50ng_Condition_A_REP3_raw.quant

[29:55] File #4/6
[29:55] Loading run /raw/LFQ_Astral_DIA_15min_50ng_Condition_B_REP1.raw
[30:22] Pre-processing...
[30:24] 2933 MS1 and 293382 MS2 scans in 978 (inferred) and 978 (encoded) cycles, 6656386 precursors in range
[30:25] Calibrating with mass accuracies 21 (MS1), 25 (MS2)
[30:52] RT window set to 1.46608
[30:52] Recommended MS1 mass accuracy setting: 2.6 ppm
[31:06] Searching decoys
[32:05] Main search
[33:47] Removing low confidence identifications
[34:22] Removing interfering precursors
[34:53] Training neural networks on 300595 target and 263633 decoy PSMs
[36:35] Training neural networks on 300595 target and 261511 decoy PSMs
[38:13] IDs at 0.01 FDR: 74063
[38:14] Precursors at 1% peptidoform FDR: 72201
[38:15] Number of IDs at 0.01 FDR: 74071
[38:15] Calculating protein q-values
[38:16] Number of proteins identified at 1% FDR: 66242 (precursor-level), 65693 (protein-level) (inference performed using proteotypic peptides only)
[38:17] Quantification
[38:18] Precursors with scored PTMs at 1% FDR: 2379 out of 2505 considered
[38:18] Precursors with all scored PTM sites unoccupied at 1% FDR: 69825
[38:18] Precursors with PTMs localised (when required) with > 90% confidence: 2295 out of 2379
[38:18] Quantification information saved to temp-DIANN_quantB/_raw_LFQ_Astral_DIA_15min_50ng_Condition_B_REP1_raw.quant

[38:18] File #5/6
[38:18] Loading run /raw/LFQ_Astral_DIA_15min_50ng_Condition_B_REP2.raw
[38:33] Pre-processing...
[38:34] 2933 MS1 and 293330 MS2 scans in 978 (inferred) and 978 (encoded) cycles, 6656386 precursors in range
[38:35] Calibrating with mass accuracies 21 (MS1), 25 (MS2)
[39:04] RT window set to 1.3432
[39:04] Recommended MS1 mass accuracy setting: 2.8 ppm
[39:13] Searching decoys
[40:14] Main search
[42:17] Removing low confidence identifications
[42:43] Removing interfering precursors
[43:02] Training neural networks on 295949 target and 257643 decoy PSMs
[44:40] Training neural networks on 295949 target and 255456 decoy PSMs
[46:30] IDs at 0.01 FDR: 72885
[46:31] Precursors at 1% peptidoform FDR: 71580
[46:33] Number of IDs at 0.01 FDR: 72885
[46:33] Calculating protein q-values
[46:35] Number of proteins identified at 1% FDR: 65310 (precursor-level), 64606 (protein-level) (inference performed using proteotypic peptides only)
[46:35] Quantification
[46:36] Precursors with scored PTMs at 1% FDR: 2354 out of 2435 considered
[46:36] Precursors with all scored PTM sites unoccupied at 1% FDR: 69226
[46:36] Precursors with PTMs localised (when required) with > 90% confidence: 2270 out of 2354
[46:38] Quantification information saved to temp-DIANN_quantB/_raw_LFQ_Astral_DIA_15min_50ng_Condition_B_REP2_raw.quant

[46:38] File #6/6
[46:38] Loading run /raw/LFQ_Astral_DIA_15min_50ng_Condition_B_REP3.raw
[47:04] Pre-processing...
[47:06] 2934 MS1 and 293446 MS2 scans in 978 (inferred) and 978 (encoded) cycles, 6656386 precursors in range
[47:07] Calibrating with mass accuracies 21 (MS1), 25 (MS2)
[47:41] RT window set to 1.44425
[47:41] Recommended MS1 mass accuracy setting: 2.5 ppm
[47:52] Searching decoys
[48:50] Main search
[50:40] Removing low confidence identifications
[51:10] Removing interfering precursors
[51:34] Training neural networks on 298712 target and 262108 decoy PSMs
[53:32] Training neural networks on 298712 target and 260734 decoy PSMs
[55:01] IDs at 0.01 FDR: 73743
[55:02] Precursors at 1% peptidoform FDR: 72089
[55:04] Number of IDs at 0.01 FDR: 73672
[55:04] Calculating protein q-values
[55:05] Number of proteins identified at 1% FDR: 65907 (precursor-level), 65093 (protein-level) (inference performed using proteotypic peptides only)
[55:05] Quantification
[55:07] Precursors with scored PTMs at 1% FDR: 2340 out of 2532 considered
[55:07] Precursors with all scored PTM sites unoccupied at 1% FDR: 69697
[55:07] Precursors with PTMs localised (when required) with > 90% confidence: 2246 out of 2340
[55:08] Quantification information saved to temp-DIANN_quantB/_raw_LFQ_Astral_DIA_15min_50ng_Condition_B_REP3_raw.quant

[55:08] Cross-run analysis
[55:08] Reading quantification information: 6 files
[55:29] Quantifying peptides
[56:13] Assembling protein groups
[56:20] Quantifying proteins
[56:21] Calculating q-values for protein and gene groups
[56:25] Calculating global q-values for protein and gene groups
[56:25] Protein groups with global q-value <= 0.01: 78055
[56:29] Compressed report saved to out-DIANN_quantB/WU2.3.2_metox_report-first-pass.parquet. Use R 'arrow' or Python 'PyArrow' package to process
[56:29] Site report saved to out-DIANN_quantB/WU2.3.2_metox_report-first-pass.site_report.parquet
[56:29] Saving precursor levels matrix
[56:29] Precursor levels matrix (1% precursor and protein group FDR) saved to out-DIANN_quantB/WU2.3.2_metox_report-first-pass.pr_matrix.tsv.
[56:29] Manifest saved to out-DIANN_quantB/WU2.3.2_metox_report-first-pass.manifest.txt
[56:29] Stats report saved to out-DIANN_quantB/WU2.3.2_metox_report-first-pass.stats.tsv
[56:30] Generating spectral library:
[56:33] 87847 target and 890 decoy precursors saved
[56:33] Spectral library saved to out-DIANN_quantB/WU2.3.2_metox_report-lib.parquet

[56:44] Loading spectral library out-DIANN_quantB/WU2.3.2_metox_report-lib.parquet
[56:45] Spectral library loaded: 80012 protein isoforms, 79848 protein groups and 88737 precursors in 82443 elution groups.
[56:45] Loading protein annotations from FASTA input/ProteoBenchFASTA_Entrapment_Human_with_contaminants_entrapment_pep.fasta
[57:10] Annotating library proteins with information from the FASTA database
[57:11] Gene names missing for some isoforms
[57:11] Library contains 80012 proteins, and 0 genes
WARNING: no gene information in the FASTA or library: consider using --ids-to-names
[57:11] Initialising library
[57:12] Saving the library to out-DIANN_quantB/WU2.3.2_metox_report-lib.parquet.skyline.speclib


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

[57:12] File #1/6
[57:12] Loading run /raw/LFQ_Astral_DIA_15min_50ng_Condition_A_REP1.raw
[57:27] Pre-processing...
[57:28] 2931 MS1 and 293271 MS2 scans in 977 (inferred) and 977 (encoded) cycles, 87847 precursors in range
[57:28] Calibrating with mass accuracies 21 (MS1), 25 (MS2)
[57:29] RT window set to 0.431317
[57:29] Recommended MS1 mass accuracy setting: 3.2 ppm
[57:29] Searching decoys
[57:30] Main search
[57:31] Removing low confidence identifications
[57:34] Removing interfering precursors
[57:35] Training neural networks on 77633 target and 37440 decoy PSMs
[57:54] Training neural networks on 77622 target and 43697 decoy PSMs
[58:12] IDs at 0.01 FDR: 76834
[58:12] Precursors at 1% peptidoform FDR: 75614
[58:12] Number of IDs at 0.01 FDR: 76837
[58:12] Calculating protein q-values
[58:12] Number of proteins identified at 1% FDR: 69382 (precursor-level), 67495 (protein-level) (inference performed using proteotypic peptides only)
[58:12] Quantification
[58:13] Precursors with scored PTMs at 1% FDR: 2117 out of 2160 considered
[58:13] Precursors with all scored PTM sites unoccupied at 1% FDR: 73498
[58:13] Precursors with PTMs localised (when required) with > 90% confidence: 2046 out of 2117

[58:13] File #2/6
[58:13] Loading run /raw/LFQ_Astral_DIA_15min_50ng_Condition_A_REP2.raw
[58:30] Pre-processing...
[58:31] 2933 MS1 and 293433 MS2 scans in 978 (inferred) and 978 (encoded) cycles, 87847 precursors in range
[58:31] Calibrating with mass accuracies 21 (MS1), 25 (MS2)
[58:32] RT window set to 0.422721
[58:32] Recommended MS1 mass accuracy setting: 3.3 ppm
[58:32] Searching decoys
[58:32] Main search
[58:33] Removing low confidence identifications
[58:36] Removing interfering precursors
[58:37] Training neural networks on 77970 target and 37431 decoy PSMs
[58:54] Training neural networks on 77956 target and 43822 decoy PSMs
[59:16] IDs at 0.01 FDR: 78193
[59:16] Precursors at 1% peptidoform FDR: 76979
[59:16] Number of IDs at 0.01 FDR: 78193
[59:16] Calculating protein q-values
[59:16] Number of proteins identified at 1% FDR: 70539 (precursor-level), 68425 (protein-level) (inference performed using proteotypic peptides only)
[59:16] Quantification
[59:17] Precursors with scored PTMs at 1% FDR: 2166 out of 2222 considered
[59:17] Precursors with all scored PTM sites unoccupied at 1% FDR: 74813
[59:17] Precursors with PTMs localised (when required) with > 90% confidence: 2089 out of 2166

[59:18] File #3/6
[59:18] Loading run /raw/LFQ_Astral_DIA_15min_50ng_Condition_A_REP3.raw
[59:33] Pre-processing...
[59:34] 2932 MS1 and 293358 MS2 scans in 978 (inferred) and 978 (encoded) cycles, 87847 precursors in range
[59:34] Calibrating with mass accuracies 21 (MS1), 25 (MS2)
[59:35] RT window set to 0.436288
[59:35] Recommended MS1 mass accuracy setting: 3.2 ppm
[59:35] Searching decoys
[59:36] Main search
[59:37] Removing low confidence identifications
[59:41] Removing interfering precursors
[59:42] Training neural networks on 77906 target and 37682 decoy PSMs
[60:03] Training neural networks on 77892 target and 43928 decoy PSMs
[60:25] IDs at 0.01 FDR: 78329
[60:25] Precursors at 1% peptidoform FDR: 77277
[60:26] Number of IDs at 0.01 FDR: 78335
[60:26] Calculating protein q-values
[60:26] Number of proteins identified at 1% FDR: 70577 (precursor-level), 68408 (protein-level) (inference performed using proteotypic peptides only)
[60:26] Quantification
[60:27] Precursors with scored PTMs at 1% FDR: 2186 out of 2229 considered
[60:27] Precursors with all scored PTM sites unoccupied at 1% FDR: 75094
[60:27] Precursors with PTMs localised (when required) with > 90% confidence: 2101 out of 2186

[60:27] File #4/6
[60:27] Loading run /raw/LFQ_Astral_DIA_15min_50ng_Condition_B_REP1.raw
[60:43] Pre-processing...
[60:44] 2933 MS1 and 293382 MS2 scans in 978 (inferred) and 978 (encoded) cycles, 87847 precursors in range
[60:44] Calibrating with mass accuracies 21 (MS1), 25 (MS2)
[60:45] RT window set to 0.444282
[60:45] Recommended MS1 mass accuracy setting: 3.4 ppm
[60:45] Searching decoys
[60:46] Main search
[60:48] Removing low confidence identifications
[60:51] Removing interfering precursors
[60:52] Training neural networks on 78745 target and 37736 decoy PSMs
[61:12] Training neural networks on 78731 target and 44451 decoy PSMs
[61:32] IDs at 0.01 FDR: 80642
[61:32] Precursors at 1% peptidoform FDR: 79728
[61:32] Number of IDs at 0.01 FDR: 80644
[61:32] Calculating protein q-values
[61:32] Number of proteins identified at 1% FDR: 72463 (precursor-level), 70218 (protein-level) (inference performed using proteotypic peptides only)
[61:32] Quantification
[61:33] Precursors with scored PTMs at 1% FDR: 2441 out of 2471 considered
[61:33] Precursors with all scored PTM sites unoccupied at 1% FDR: 77288
[61:33] Precursors with PTMs localised (when required) with > 90% confidence: 2377 out of 2441

[61:34] File #5/6
[61:34] Loading run /raw/LFQ_Astral_DIA_15min_50ng_Condition_B_REP2.raw
[61:49] Pre-processing...
[61:50] 2933 MS1 and 293330 MS2 scans in 978 (inferred) and 978 (encoded) cycles, 87847 precursors in range
[61:50] Calibrating with mass accuracies 21 (MS1), 25 (MS2)
[61:50] RT window set to 0.442049
[61:50] Recommended MS1 mass accuracy setting: 3.2 ppm
[61:50] Searching decoys
[61:51] Main search
[61:52] Removing low confidence identifications
[61:55] Removing interfering precursors
[61:56] Training neural networks on 78788 target and 37804 decoy PSMs
[62:11] Training neural networks on 78778 target and 44466 decoy PSMs
[62:39] IDs at 0.01 FDR: 80759
[62:39] Precursors at 1% peptidoform FDR: 79804
[62:39] Number of IDs at 0.01 FDR: 80759
[62:39] Calculating protein q-values
[62:39] Number of proteins identified at 1% FDR: 72622 (precursor-level), 70089 (protein-level) (inference performed using proteotypic peptides only)
[62:39] Quantification
[62:41] Precursors with scored PTMs at 1% FDR: 2451 out of 2469 considered
[62:41] Precursors with all scored PTM sites unoccupied at 1% FDR: 77353
[62:41] Precursors with PTMs localised (when required) with > 90% confidence: 2379 out of 2451

[62:41] File #6/6
[62:41] Loading run /raw/LFQ_Astral_DIA_15min_50ng_Condition_B_REP3.raw
[63:01] Pre-processing...
[63:01] 2934 MS1 and 293446 MS2 scans in 978 (inferred) and 978 (encoded) cycles, 87847 precursors in range
[63:01] Calibrating with mass accuracies 21 (MS1), 25 (MS2)
[63:02] RT window set to 0.456031
[63:02] Recommended MS1 mass accuracy setting: 3.2 ppm
[63:02] Searching decoys
[63:03] Main search
[63:04] Removing low confidence identifications
[63:08] Removing interfering precursors
[63:10] Training neural networks on 78780 target and 37656 decoy PSMs
[63:34] Training neural networks on 78769 target and 44245 decoy PSMs
[63:58] IDs at 0.01 FDR: 80796
[63:58] Precursors at 1% peptidoform FDR: 79823
[63:58] Number of IDs at 0.01 FDR: 80796
[63:58] Calculating protein q-values
[63:58] Number of proteins identified at 1% FDR: 72665 (precursor-level), 70506 (protein-level) (inference performed using proteotypic peptides only)
[63:58] Quantification
[63:59] Precursors with scored PTMs at 1% FDR: 2439 out of 2470 considered
[63:59] Precursors with all scored PTM sites unoccupied at 1% FDR: 77384
[63:59] Precursors with PTMs localised (when required) with > 90% confidence: 2361 out of 2439

[64:00] Cross-run analysis
[64:00] Reading quantification information: 6 files
[64:03] Quantifying peptides
[64:57] Quantification parameters: 0.359756, 0.00162556, 0.00156069, 0.0132813, 0.12356, 0.0398731, 0.277853, 0.1148, 0.155964, 0.0150457, 0.0481054, 0.0427237, 0.358333, 0.0523838, 0.0688471, 0.0117667
[65:09] Quantifying proteins
[65:10] Calculating q-values for protein and gene groups
[65:10] Calculating global q-values for protein and gene groups
[65:10] Protein groups with global q-value <= 0.01: 76055
[65:14] Compressed report saved to out-DIANN_quantB/WU2.3.2_metox_report.parquet. Use R 'arrow' or Python 'PyArrow' package to process
[65:14] Site report saved to out-DIANN_quantB/WU2.3.2_metox_report.site_report.parquet
[65:14] Saving precursor levels matrix
[65:14] Precursor levels matrix (1% precursor and protein group FDR) saved to out-DIANN_quantB/WU2.3.2_metox_report.pr_matrix.tsv.
[65:14] Saving protein group levels matrix
[65:14] Protein groups matrix saved to out-DIANN_quantB/WU2.3.2_metox_report.pg_matrix.tsv.
[65:14] Saving gene group levels matrix
[65:14] Gene groups matrix saved to out-DIANN_quantB/WU2.3.2_metox_report.gg_matrix.tsv.
[65:14] Saving unique genes levels matrix
[65:15] Unique genes matrix saved to out-DIANN_quantB/WU2.3.2_metox_report.unique_genes_matrix.tsv.
[65:15] Manifest saved to out-DIANN_quantB/WU2.3.2_metox_report.manifest.txt
[65:15] Stats report saved to out-DIANN_quantB/WU2.3.2_metox_report.stats.tsv

