
DIA-NN 2.5.1 Academia  (Data-Independent Acquisition by Neural Networks)
Compiled on Apr 30 2026 02:32:47
Current date and time: Thu Jul 23 23:46:19 2026
Logical CPU cores: 128
/opt/diann-2.5.1/diann-linux --lib out-DIANN_libA/WU2.5.1_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/WU2.5.1_metox_report-lib.parquet --out out-DIANN_quantB/WU2.5.1_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/WU2.5.1_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 (targets and decoys).
[0:07] Loading FASTA input/ProteoBenchFASTA_Entrapment_Human_with_contaminants_entrapment_pep.fasta
[1:28] Reannotating library precursors with information from the FASTA database
[1:57] Finding proteotypic peptides (assuming that the list of UniProt ids provided for each peptide is complete)
[1:58] 6661787 precursors generated
[2:02] Gene names missing for some isoforms
[2:02] Library contains 2655626 proteins, and 0 genes
WARNING: no gene information in the FASTA or library: consider using --ids-to-names
[2:11] Initialising library

First pass: generating a spectral library from DIA data

[2:33] File #1/6
[2:33] Loading run /raw/LFQ_Astral_DIA_15min_50ng_Condition_A_REP1.raw
[2:50] Pre-processing...
[2:51] 2931 MS1 and 293271 MS2 scans in 977 (inferred) and 977 (encoded) cycles, 6656386 precursors in range
[2:52] Calibrating with mass accuracies 21 (MS1), 25 (MS2)
[3:26] RT window set to 1.5022
[3:26] Peak width: 2.824
[3:26] Scan window radius set to 6
[3:26] Recommended MS1 mass accuracy setting: 2.7 ppm
[4:05] Optimised mass accuracy: 8 ppm
[4:18] Searching decoys
[5:03] Main search
[6:30] Removing low confidence identifications
[6:51] Removing interfering precursors
[7:05] Training neural networks on 267452 target and 229380 decoy PSMs
[8:02] Training neural networks on 267452 target and 228452 decoy PSMs
[8:54] Precursors at 1% peptidoform FDR: 66143
[8:56] Number of IDs at 0.01 FDR: 67106
[8:56] Calculating protein q-values
[8:57] Number of proteins identified at 1% FDR: 60864 (precursor-level), 60114 (protein-level) (inference performed using proteotypic peptides only)
[8:57] Quantification
[8:59] Precursors with scored PTMs at 1% FDR: 1631 out of 1688 considered
[8:59] Precursors with all scored PTM sites unoccupied at 1% FDR: 64512
[8:59] Precursors with PTMs localised (when required) with > 90% confidence: 1579 out of 1631
[8:59] Quantification information saved to temp-DIANN_quantB/_raw_LFQ_Astral_DIA_15min_50ng_Condition_A_REP1_raw.quant

[9:00] File #2/6
[9:00] Loading run /raw/LFQ_Astral_DIA_15min_50ng_Condition_A_REP2.raw
[9:15] Pre-processing...
[9:17] 2933 MS1 and 293433 MS2 scans in 978 (inferred) and 978 (encoded) cycles, 6656386 precursors in range
[9:17] Calibrating with mass accuracies 21 (MS1), 25 (MS2)
[9:49] RT window set to 1.53602
[9:49] Recommended MS1 mass accuracy setting: 2.5 ppm
[10:00] Searching decoys
[10:46] Main search
[12:13] Removing low confidence identifications
[12:36] Removing interfering precursors
[12:48] Training neural networks on 255096 target and 211831 decoy PSMs
[13:42] Training neural networks on 255096 target and 211820 decoy PSMs
[14:31] Precursors at 1% peptidoform FDR: 68043
[14:33] Number of IDs at 0.01 FDR: 69446
[14:33] Calculating protein q-values
[14:34] Number of proteins identified at 1% FDR: 62795 (precursor-level), 61842 (protein-level) (inference performed using proteotypic peptides only)
[14:34] Quantification
[14:35] Precursors with scored PTMs at 1% FDR: 1679 out of 1802 considered
[14:35] Precursors with all scored PTM sites unoccupied at 1% FDR: 66364
[14:35] Precursors with PTMs localised (when required) with > 90% confidence: 1622 out of 1679
[14:36] Quantification information saved to temp-DIANN_quantB/_raw_LFQ_Astral_DIA_15min_50ng_Condition_A_REP2_raw.quant

[14:36] File #3/6
[14:36] Loading run /raw/LFQ_Astral_DIA_15min_50ng_Condition_A_REP3.raw
[14:52] Pre-processing...
[14:54] 2932 MS1 and 293358 MS2 scans in 978 (inferred) and 978 (encoded) cycles, 6656386 precursors in range
[14:54] Calibrating with mass accuracies 21 (MS1), 25 (MS2)
[15:26] RT window set to 1.45569
[15:27] Recommended MS1 mass accuracy setting: 2.5 ppm
[15:39] Searching decoys
[16:24] Main search
[17:50] Removing low confidence identifications
[18:11] Removing interfering precursors
[18:23] Training neural networks on 267069 target and 224987 decoy PSMs
[19:21] Training neural networks on 267069 target and 223860 decoy PSMs
[20:13] Precursors at 1% peptidoform FDR: 68179
[20:15] Number of IDs at 0.01 FDR: 70044
[20:15] Calculating protein q-values
[20:17] Number of proteins identified at 1% FDR: 63344 (precursor-level), 62530 (protein-level) (inference performed using proteotypic peptides only)
[20:17] Quantification
[20:18] Precursors with scored PTMs at 1% FDR: 1681 out of 1824 considered
[20:18] Precursors with all scored PTM sites unoccupied at 1% FDR: 66498
[20:18] Precursors with PTMs localised (when required) with > 90% confidence: 1616 out of 1681
[20:19] Quantification information saved to temp-DIANN_quantB/_raw_LFQ_Astral_DIA_15min_50ng_Condition_A_REP3_raw.quant

[20:19] File #4/6
[20:19] Loading run /raw/LFQ_Astral_DIA_15min_50ng_Condition_B_REP1.raw
[20:36] Pre-processing...
[20:37] 2933 MS1 and 293382 MS2 scans in 978 (inferred) and 978 (encoded) cycles, 6656386 precursors in range
[20:38] Calibrating with mass accuracies 21 (MS1), 25 (MS2)
[21:04] RT window set to 1.46608
[21:04] Recommended MS1 mass accuracy setting: 2.6 ppm
[21:15] Searching decoys
[22:01] Main search
[23:28] Removing low confidence identifications
[23:50] Removing interfering precursors
[24:03] Training neural networks on 276544 target and 234571 decoy PSMs
[25:02] Training neural networks on 276544 target and 233014 decoy PSMs
[25:56] Precursors at 1% peptidoform FDR: 73279
[25:58] Number of IDs at 0.01 FDR: 74926
[25:58] Calculating protein q-values
[25:59] Number of proteins identified at 1% FDR: 67110 (precursor-level), 66173 (protein-level) (inference performed using proteotypic peptides only)
[25:59] Quantification
[26:00] Precursors with scored PTMs at 1% FDR: 2361 out of 2466 considered
[26:00] Precursors with all scored PTM sites unoccupied at 1% FDR: 70918
[26:00] Precursors with PTMs localised (when required) with > 90% confidence: 2276 out of 2361
[26:01] Quantification information saved to temp-DIANN_quantB/_raw_LFQ_Astral_DIA_15min_50ng_Condition_B_REP1_raw.quant

[26:01] File #5/6
[26:01] Loading run /raw/LFQ_Astral_DIA_15min_50ng_Condition_B_REP2.raw
[26:18] Pre-processing...
[26:19] 2933 MS1 and 293330 MS2 scans in 978 (inferred) and 978 (encoded) cycles, 6656386 precursors in range
[26:20] Calibrating with mass accuracies 21 (MS1), 25 (MS2)
[26:46] RT window set to 1.3432
[26:46] Recommended MS1 mass accuracy setting: 2.8 ppm
[26:55] Searching decoys
[27:38] Main search
[28:58] Removing low confidence identifications
[29:19] Removing interfering precursors
[29:32] Training neural networks on 272198 target and 229204 decoy PSMs
[30:29] Training neural networks on 272198 target and 228013 decoy PSMs
[31:22] Precursors at 1% peptidoform FDR: 72384
[31:24] Number of IDs at 0.01 FDR: 73807
[31:24] Calculating protein q-values
[31:25] Number of proteins identified at 1% FDR: 66197 (precursor-level), 65334 (protein-level) (inference performed using proteotypic peptides only)
[31:26] Quantification
[31:27] Precursors with scored PTMs at 1% FDR: 2317 out of 2403 considered
[31:27] Precursors with all scored PTM sites unoccupied at 1% FDR: 70067
[31:27] Precursors with PTMs localised (when required) with > 90% confidence: 2237 out of 2317
[31:28] Quantification information saved to temp-DIANN_quantB/_raw_LFQ_Astral_DIA_15min_50ng_Condition_B_REP2_raw.quant

[31:28] File #6/6
[31:28] Loading run /raw/LFQ_Astral_DIA_15min_50ng_Condition_B_REP3.raw
[31:43] Pre-processing...
[31:45] 2934 MS1 and 293446 MS2 scans in 978 (inferred) and 978 (encoded) cycles, 6656386 precursors in range
[31:45] Calibrating with mass accuracies 21 (MS1), 25 (MS2)
[32:11] RT window set to 1.44425
[32:12] Recommended MS1 mass accuracy setting: 2.5 ppm
[32:22] Searching decoys
[33:06] Main search
[34:30] Removing low confidence identifications
[34:51] Removing interfering precursors
[35:04] Training neural networks on 276376 target and 234972 decoy PSMs
[36:02] Training neural networks on 276376 target and 234409 decoy PSMs
[36:57] Precursors at 1% peptidoform FDR: 73257
[36:59] Number of IDs at 0.01 FDR: 74826
[36:59] Calculating protein q-values
[37:00] Number of proteins identified at 1% FDR: 67058 (precursor-level), 66054 (protein-level) (inference performed using proteotypic peptides only)
[37:00] Quantification
[37:02] Precursors with scored PTMs at 1% FDR: 2356 out of 2530 considered
[37:02] Precursors with all scored PTM sites unoccupied at 1% FDR: 70901
[37:02] Precursors with PTMs localised (when required) with > 90% confidence: 2260 out of 2356
[37:03] Quantification information saved to temp-DIANN_quantB/_raw_LFQ_Astral_DIA_15min_50ng_Condition_B_REP3_raw.quant

[37:03] Cross-run analysis
[37:03] Reading quantification information: 6 files
[37:20] Target precursors at 1% global q-value: 88676
[37:20] Quantifying peptides
[37:46] Assembling protein groups
[37:52] Quantifying proteins
[37:54] Calculating q-values for protein and gene groups
[37:56] Calculating global q-values for protein and gene groups
[37:56] Protein groups with global q-value <= 0.01: 78646
[37:59] Compressed report saved to out-DIANN_quantB/WU2.5.1_metox_report-first-pass.parquet. Use R 'arrow' or Python 'PyArrow' package to process
[37:59] Site report saved to out-DIANN_quantB/WU2.5.1_metox_report-first-pass.site_report.parquet
[37:59] Saving precursor levels matrix
[38:00] Precursor levels matrix (1% precursor and protein group FDR) saved to out-DIANN_quantB/WU2.5.1_metox_report-first-pass.pr_matrix.tsv.
[38:00] Manifest saved to out-DIANN_quantB/WU2.5.1_metox_report-first-pass.manifest.txt
[38:00] Stats report saved to out-DIANN_quantB/WU2.5.1_metox_report-first-pass.stats.tsv
[38:00] Generating spectral library:
[38:02] 108826 target and 6161 decoy precursors saved
[38:03] Spectral library saved to out-DIANN_quantB/WU2.5.1_metox_report-lib.parquet

[38:10] Loading spectral library out-DIANN_quantB/WU2.5.1_metox_report-lib.parquet
[38:12] Spectral library loaded: 103318 protein isoforms, 103084 protein groups and 114987 precursors in 107137 elution groups (targets and decoys).
[38:12] Loading protein annotations from FASTA input/ProteoBenchFASTA_Entrapment_Human_with_contaminants_entrapment_pep.fasta
[38:34] Annotating library proteins with information from the FASTA database
[38:35] Gene names missing for some isoforms
[38:35] Library contains 103318 proteins, and 0 genes
WARNING: no gene information in the FASTA or library: consider using --ids-to-names
[38:36] Initialising library
[38:37] Saving the library to out-DIANN_quantB/WU2.5.1_metox_report-lib.parquet.skyline.speclib


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

[38:37] File #1/6
[38:37] Loading run /raw/LFQ_Astral_DIA_15min_50ng_Condition_A_REP1.raw
[38:52] Pre-processing...
[38:53] 2931 MS1 and 293271 MS2 scans in 977 (inferred) and 977 (encoded) cycles, 108826 precursors in range
[38:53] Calibrating with mass accuracies 21 (MS1), 25 (MS2)
[38:53] RT window set to 0.428049
[38:54] Recommended MS1 mass accuracy setting: 3.1 ppm
[38:54] Searching decoys
[38:55] Main search
[38:57] Removing low confidence identifications
[39:00] Removing interfering precursors
[39:01] Training neural networks on 98851 target and 44981 decoy PSMs
[39:15] Training neural networks on 98605 target and 51789 decoy PSMs
[39:30] Precursors at 1% peptidoform FDR: 77054
[39:30] Number of IDs at 0.01 FDR: 78428
[39:30] Calculating protein q-values
[39:30] Number of proteins identified at 1% FDR: 70733 (precursor-level), 70802 (protein-level) (inference performed using proteotypic peptides only)
[39:30] Quantification
[39:31] Precursors with scored PTMs at 1% FDR: 2100 out of 2190 considered
[39:31] Precursors with all scored PTM sites unoccupied at 1% FDR: 74955
[39:31] Precursors with PTMs localised (when required) with > 90% confidence: 2025 out of 2100

[39:32] File #2/6
[39:32] Loading run /raw/LFQ_Astral_DIA_15min_50ng_Condition_A_REP2.raw
[39:48] Pre-processing...
[39:49] 2933 MS1 and 293433 MS2 scans in 978 (inferred) and 978 (encoded) cycles, 108826 precursors in range
[39:49] Calibrating with mass accuracies 21 (MS1), 25 (MS2)
[39:49] RT window set to 0.429442
[39:49] Recommended MS1 mass accuracy setting: 3 ppm
[39:49] Searching decoys
[39:50] Main search
[39:52] Removing low confidence identifications
[39:55] Removing interfering precursors
[39:57] Training neural networks on 99447 target and 45262 decoy PSMs
[40:11] Training neural networks on 99227 target and 51921 decoy PSMs
[40:25] Precursors at 1% peptidoform FDR: 78387
[40:26] Number of IDs at 0.01 FDR: 79700
[40:26] Calculating protein q-values
[40:26] Number of proteins identified at 1% FDR: 71829 (precursor-level), 71845 (protein-level) (inference performed using proteotypic peptides only)
[40:26] Quantification
[40:26] Precursors with scored PTMs at 1% FDR: 2137 out of 2246 considered
[40:26] Precursors with all scored PTM sites unoccupied at 1% FDR: 76250
[40:26] Precursors with PTMs localised (when required) with > 90% confidence: 2066 out of 2137

[40:27] File #3/6
[40:27] Loading run /raw/LFQ_Astral_DIA_15min_50ng_Condition_A_REP3.raw
[40:43] Pre-processing...
[40:44] 2932 MS1 and 293358 MS2 scans in 978 (inferred) and 978 (encoded) cycles, 108826 precursors in range
[40:44] Calibrating with mass accuracies 21 (MS1), 25 (MS2)
[40:44] RT window set to 0.447246
[40:44] Recommended MS1 mass accuracy setting: 2.9 ppm
[40:45] Searching decoys
[40:46] Main search
[40:48] Removing low confidence identifications
[40:51] Removing interfering precursors
[40:52] Training neural networks on 99322 target and 44297 decoy PSMs
[41:06] Training neural networks on 99088 target and 51739 decoy PSMs
[41:21] Precursors at 1% peptidoform FDR: 78457
[41:21] Number of IDs at 0.01 FDR: 80032
[41:21] Calculating protein q-values
[41:21] Number of proteins identified at 1% FDR: 72099 (precursor-level), 72217 (protein-level) (inference performed using proteotypic peptides only)
[41:21] Quantification
[41:22] Precursors with scored PTMs at 1% FDR: 2160 out of 2260 considered
[41:22] Precursors with all scored PTM sites unoccupied at 1% FDR: 76306
[41:22] Precursors with PTMs localised (when required) with > 90% confidence: 2081 out of 2160

[41:22] File #4/6
[41:22] Loading run /raw/LFQ_Astral_DIA_15min_50ng_Condition_B_REP1.raw
[41:39] Pre-processing...
[41:40] 2933 MS1 and 293382 MS2 scans in 978 (inferred) and 978 (encoded) cycles, 108826 precursors in range
[41:40] Calibrating with mass accuracies 21 (MS1), 25 (MS2)
[41:41] RT window set to 0.461267
[41:41] Recommended MS1 mass accuracy setting: 3.1 ppm
[41:41] Searching decoys
[41:42] Main search
[41:44] Removing low confidence identifications
[41:47] Removing interfering precursors
[41:49] Training neural networks on 100574 target and 45001 decoy PSMs
[42:03] Training neural networks on 100336 target and 52895 decoy PSMs
[42:18] Precursors at 1% peptidoform FDR: 80619
[42:18] Number of IDs at 0.01 FDR: 82147
[42:18] Calculating protein q-values
[42:18] Number of proteins identified at 1% FDR: 73771 (precursor-level), 73885 (protein-level) (inference performed using proteotypic peptides only)
[42:18] Quantification
[42:19] Precursors with scored PTMs at 1% FDR: 2379 out of 2510 considered
[42:19] Precursors with all scored PTM sites unoccupied at 1% FDR: 78266
[42:19] Precursors with PTMs localised (when required) with > 90% confidence: 2319 out of 2379

[42:19] File #5/6
[42:19] Loading run /raw/LFQ_Astral_DIA_15min_50ng_Condition_B_REP2.raw
[42:36] Pre-processing...
[42:36] 2933 MS1 and 293330 MS2 scans in 978 (inferred) and 978 (encoded) cycles, 108826 precursors in range
[42:36] Calibrating with mass accuracies 21 (MS1), 25 (MS2)
[42:37] RT window set to 0.459123
[42:37] Recommended MS1 mass accuracy setting: 3.1 ppm
[42:37] Searching decoys
[42:38] Main search
[42:40] Removing low confidence identifications
[42:44] Removing interfering precursors
[42:45] Training neural networks on 100556 target and 45534 decoy PSMs
[43:00] Training neural networks on 100331 target and 52769 decoy PSMs
[43:14] Precursors at 1% peptidoform FDR: 80241
[43:14] Number of IDs at 0.01 FDR: 81621
[43:14] Calculating protein q-values
[43:15] Number of proteins identified at 1% FDR: 73377 (precursor-level), 73613 (protein-level) (inference performed using proteotypic peptides only)
[43:15] Quantification
[43:15] Precursors with scored PTMs at 1% FDR: 2353 out of 2489 considered
[43:15] Precursors with all scored PTM sites unoccupied at 1% FDR: 77888
[43:15] Precursors with PTMs localised (when required) with > 90% confidence: 2291 out of 2353

[43:16] File #6/6
[43:16] Loading run /raw/LFQ_Astral_DIA_15min_50ng_Condition_B_REP3.raw
[43:32] Pre-processing...
[43:32] 2934 MS1 and 293446 MS2 scans in 978 (inferred) and 978 (encoded) cycles, 108826 precursors in range
[43:32] Calibrating with mass accuracies 21 (MS1), 25 (MS2)
[43:33] RT window set to 0.461681
[43:33] Recommended MS1 mass accuracy setting: 3 ppm
[43:33] Searching decoys
[43:34] Main search
[43:36] Removing low confidence identifications
[43:40] Removing interfering precursors
[43:41] Training neural networks on 100518 target and 45447 decoy PSMs
[43:55] Training neural networks on 100292 target and 52892 decoy PSMs
[44:10] Precursors at 1% peptidoform FDR: 80755
[44:10] Number of IDs at 0.01 FDR: 82242
[44:10] Calculating protein q-values
[44:10] Number of proteins identified at 1% FDR: 73880 (precursor-level), 73976 (protein-level) (inference performed using proteotypic peptides only)
[44:10] Quantification
[44:11] Precursors with scored PTMs at 1% FDR: 2402 out of 2532 considered
[44:11] Precursors with all scored PTM sites unoccupied at 1% FDR: 78370
[44:11] Precursors with PTMs localised (when required) with > 90% confidence: 2322 out of 2402

[44:12] Cross-run analysis
[44:12] Reading quantification information: 6 files
[44:14] Target precursors at 1% global q-value: 87232
[44:14] Quantifying peptides
[45:01] Quantification parameters: 0.361137, 0.00162927, 0.00157403, 0.0134991, 0.119366, 0.0180038, 0.289955, 0.127801, 0.146875, 0.0152751, 0.0481639, 0.0422623, 0.373717, 0.0508353, 0.0651278, 0.0113253
[45:12] Quantifying proteins
[45:12] Calculating q-values for protein and gene groups
[45:13] Calculating global q-values for protein and gene groups
[45:13] Protein groups with global q-value <= 0.01: 78181
[45:15] Compressed report saved to out-DIANN_quantB/WU2.5.1_metox_report.parquet. Use R 'arrow' or Python 'PyArrow' package to process
[45:15] Site report saved to out-DIANN_quantB/WU2.5.1_metox_report.site_report.parquet
[45:15] Saving precursor levels matrix
[45:16] Precursor levels matrix (1% precursor and protein group FDR) saved to out-DIANN_quantB/WU2.5.1_metox_report.pr_matrix.tsv.
[45:16] Saving protein group levels matrix
[45:16] Protein groups matrix saved to out-DIANN_quantB/WU2.5.1_metox_report.pg_matrix.tsv.
[45:16] Saving gene group levels matrix
[45:16] Gene groups matrix saved to out-DIANN_quantB/WU2.5.1_metox_report.gg_matrix.tsv.
[45:16] Saving unique genes levels matrix
[45:16] Unique genes matrix saved to out-DIANN_quantB/WU2.5.1_metox_report.unique_genes_matrix.tsv.
[45:16] Manifest saved to out-DIANN_quantB/WU2.5.1_metox_report.manifest.txt
[45:16] Stats report saved to out-DIANN_quantB/WU2.5.1_metox_report.stats.tsv

