
DIA-NN 2.6.0 Academia  (Data-Independent Acquisition by Neural Networks)
Compiled on Jun 10 2026 15:42:45
Current date and time: Fri Jul 24 01:53:03 2026
Logical CPU cores: 128
/opt/diann-2.6.0/diann-linux --lib out-DIANN_libA/WU2.6.0_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/WU2.6.0_metox_report-lib.parquet --out out-DIANN_quantB/WU2.6.0_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/WU2.6.0_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
[2:22] Reannotating library precursors with information from the FASTA database
[3:03] Finding proteotypic peptides (assuming that the list of UniProt ids provided for each peptide is complete)
[3:03] 6661787 precursors generated
[3:07] Gene names missing for some isoforms
[3:07] Library contains 2655626 proteins, and 0 genes
WARNING: no gene information in the FASTA or library: consider using --ids-to-names
[3:16] Initialising library

First pass: generating a spectral library from DIA data

[3:44] File #1/6
[3:44] Loading run /raw/LFQ_Astral_DIA_15min_50ng_Condition_A_REP1.raw
[4:01] Pre-processing...
[4:03] 2931 MS1 and 293271 MS2 scans in 977 (inferred) and 977 (encoded) cycles, 6656386 precursors in range
[4:04] Calibrating with mass accuracies 21 (MS1), 25 (MS2)
[4:44] RT window set to 1.26951
[4:44] Peak width: 2.824
[4:44] Scan window radius set to 6
[4:44] Recommended MS1 mass accuracy setting: 2.3 ppm
[5:14] Optimised mass accuracy: 6 ppm
[5:25] Searching decoys
[5:57] Main search
[6:59] Removing low confidence identifications
[7:17] Removing interfering precursors
[7:30] Training neural networks on 268773 target and 224875 decoy PSMs
[8:25] Training neural networks on 268773 target and 224462 decoy PSMs
[9:17] Precursors at 1% peptidoform FDR: 65966
[9:20] Number of IDs at 0.01 FDR: 67334
[9:20] Calculating protein q-values
[9:21] Number of proteins identified at 1% FDR: 60967 (precursor-level), 60128 (protein-level) (inference performed using proteotypic peptides only)
[9:21] Quantification
[9:22] Precursors with scored PTMs at 1% FDR: 1633 out of 1753 considered
[9:22] Precursors with all scored PTM sites unoccupied at 1% FDR: 64333
[9:22] Precursors with PTMs localised (when required) with > 90% confidence: 1578 out of 1633
[9:23] Quantification information saved to temp-DIANN_quantB/_raw_LFQ_Astral_DIA_15min_50ng_Condition_A_REP1_raw.quant

[9:23] File #2/6
[9:23] Loading run /raw/LFQ_Astral_DIA_15min_50ng_Condition_A_REP2.raw
[9:37] Pre-processing...
[9:38] 2933 MS1 and 293433 MS2 scans in 978 (inferred) and 978 (encoded) cycles, 6656386 precursors in range
[9:39] Calibrating with mass accuracies 21 (MS1), 25 (MS2)
[10:08] RT window set to 1.20903
[10:09] Recommended MS1 mass accuracy setting: 1.9 ppm
[10:16] Searching decoys
[10:46] Main search
[11:43] Removing low confidence identifications
[12:01] Removing interfering precursors
[12:14] Training neural networks on 268761 target and 222121 decoy PSMs
[13:09] Training neural networks on 268761 target and 221904 decoy PSMs
[14:00] Precursors at 1% peptidoform FDR: 66925
[14:02] Number of IDs at 0.01 FDR: 68690
[14:02] Calculating protein q-values
[14:03] Number of proteins identified at 1% FDR: 62111 (precursor-level), 61247 (protein-level) (inference performed using proteotypic peptides only)
[14:04] Quantification
[14:05] Precursors with scored PTMs at 1% FDR: 1615 out of 1762 considered
[14:05] Precursors with all scored PTM sites unoccupied at 1% FDR: 65310
[14:05] Precursors with PTMs localised (when required) with > 90% confidence: 1557 out of 1615
[14:06] Quantification information saved to temp-DIANN_quantB/_raw_LFQ_Astral_DIA_15min_50ng_Condition_A_REP2_raw.quant

[14:06] File #3/6
[14:06] Loading run /raw/LFQ_Astral_DIA_15min_50ng_Condition_A_REP3.raw
[14:18] Pre-processing...
[14:19] 2932 MS1 and 293358 MS2 scans in 978 (inferred) and 978 (encoded) cycles, 6656386 precursors in range
[14:20] Calibrating with mass accuracies 21 (MS1), 25 (MS2)
[14:50] RT window set to 1.23117
[14:50] Recommended MS1 mass accuracy setting: 2.1 ppm
[14:58] Searching decoys
[15:29] Main search
[16:28] Removing low confidence identifications
[16:46] Removing interfering precursors
[16:59] Training neural networks on 273081 target and 227876 decoy PSMs
[17:55] Training neural networks on 273081 target and 227685 decoy PSMs
[18:46] Precursors at 1% peptidoform FDR: 66998
[18:49] Number of IDs at 0.01 FDR: 69080
[18:49] Calculating protein q-values
[18:50] Number of proteins identified at 1% FDR: 62483 (precursor-level), 61669 (protein-level) (inference performed using proteotypic peptides only)
[18:50] Quantification
[18:51] Precursors with scored PTMs at 1% FDR: 1653 out of 1794 considered
[18:51] Precursors with all scored PTM sites unoccupied at 1% FDR: 65345
[18:51] Precursors with PTMs localised (when required) with > 90% confidence: 1595 out of 1653
[18:52] Quantification information saved to temp-DIANN_quantB/_raw_LFQ_Astral_DIA_15min_50ng_Condition_A_REP3_raw.quant

[18:52] File #4/6
[18:52] Loading run /raw/LFQ_Astral_DIA_15min_50ng_Condition_B_REP1.raw
[19:05] Pre-processing...
[19:06] 2933 MS1 and 293382 MS2 scans in 978 (inferred) and 978 (encoded) cycles, 6656386 precursors in range
[19:07] Calibrating with mass accuracies 21 (MS1), 25 (MS2)
[19:31] RT window set to 1.22445
[19:31] Recommended MS1 mass accuracy setting: 2.2 ppm
[19:39] Searching decoys
[20:10] Main search
[21:10] Removing low confidence identifications
[21:29] Removing interfering precursors
[21:43] Training neural networks on 295890 target and 251486 decoy PSMs
[22:44] Training neural networks on 295890 target and 250360 decoy PSMs
[23:40] Precursors at 1% peptidoform FDR: 72192
[23:42] Number of IDs at 0.01 FDR: 73829
[23:42] Calculating protein q-values
[23:43] Number of proteins identified at 1% FDR: 66137 (precursor-level), 65171 (protein-level) (inference performed using proteotypic peptides only)
[23:44] Quantification
[23:45] Precursors with scored PTMs at 1% FDR: 2262 out of 2381 considered
[23:45] Precursors with all scored PTM sites unoccupied at 1% FDR: 69930
[23:45] Precursors with PTMs localised (when required) with > 90% confidence: 2176 out of 2262
[23:46] Quantification information saved to temp-DIANN_quantB/_raw_LFQ_Astral_DIA_15min_50ng_Condition_B_REP1_raw.quant

[23:46] File #5/6
[23:46] Loading run /raw/LFQ_Astral_DIA_15min_50ng_Condition_B_REP2.raw
[23:59] Pre-processing...
[24:00] 2933 MS1 and 293330 MS2 scans in 978 (inferred) and 978 (encoded) cycles, 6656386 precursors in range
[24:01] Calibrating with mass accuracies 21 (MS1), 25 (MS2)
[24:24] RT window set to 1.21421
[24:24] Recommended MS1 mass accuracy setting: 2.1 ppm
[24:31] Searching decoys
[25:01] Main search
[26:00] Removing low confidence identifications
[26:18] Removing interfering precursors
[26:33] Training neural networks on 282769 target and 236295 decoy PSMs
[27:31] Training neural networks on 282769 target and 235977 decoy PSMs
[28:24] Precursors at 1% peptidoform FDR: 71816
[28:27] Number of IDs at 0.01 FDR: 72986
[28:27] Calculating protein q-values
[28:28] Number of proteins identified at 1% FDR: 65514 (precursor-level), 64743 (protein-level) (inference performed using proteotypic peptides only)
[28:29] Quantification
[28:30] Precursors with scored PTMs at 1% FDR: 2272 out of 2361 considered
[28:30] Precursors with all scored PTM sites unoccupied at 1% FDR: 69544
[28:30] Precursors with PTMs localised (when required) with > 90% confidence: 2195 out of 2272
[28:31] Quantification information saved to temp-DIANN_quantB/_raw_LFQ_Astral_DIA_15min_50ng_Condition_B_REP2_raw.quant

[28:31] File #6/6
[28:31] Loading run /raw/LFQ_Astral_DIA_15min_50ng_Condition_B_REP3.raw
[28:44] Pre-processing...
[28:46] 2934 MS1 and 293446 MS2 scans in 978 (inferred) and 978 (encoded) cycles, 6656386 precursors in range
[28:46] Calibrating with mass accuracies 21 (MS1), 25 (MS2)
[29:10] RT window set to 1.23877
[29:10] Recommended MS1 mass accuracy setting: 2.2 ppm
[29:19] Searching decoys
[29:51] Main search
[30:49] Removing low confidence identifications
[31:08] Removing interfering precursors
[31:21] Training neural networks on 284783 target and 237650 decoy PSMs
[32:20] Training neural networks on 284783 target and 237472 decoy PSMs
[33:14] Precursors at 1% peptidoform FDR: 72475
[33:16] Number of IDs at 0.01 FDR: 74053
[33:16] Calculating protein q-values
[33:18] Number of proteins identified at 1% FDR: 66324 (precursor-level), 65625 (protein-level) (inference performed using proteotypic peptides only)
[33:18] Quantification
[33:19] Precursors with scored PTMs at 1% FDR: 2396 out of 2489 considered
[33:19] Precursors with all scored PTM sites unoccupied at 1% FDR: 70079
[33:19] Precursors with PTMs localised (when required) with > 90% confidence: 2300 out of 2396
[33:20] Quantification information saved to temp-DIANN_quantB/_raw_LFQ_Astral_DIA_15min_50ng_Condition_B_REP3_raw.quant

[33:20] Cross-run analysis
[33:20] Reading quantification information: 6 files
[33:38] Target precursors at 1% global q-value: 87846
[33:39] Quantifying peptides
[34:06] Assembling protein groups
[34:12] Quantifying proteins
[34:14] Calculating q-values for protein and gene groups
[34:16] Calculating global q-values for protein and gene groups
[34:16] Protein groups with global q-value <= 0.01: 77939
[34:19] Compressed report saved to out-DIANN_quantB/WU2.6.0_metox_report-first-pass.parquet. Use R 'arrow' or Python 'PyArrow' package to process
[34:19] Site report saved to out-DIANN_quantB/WU2.6.0_metox_report-first-pass.site_report.parquet
[34:19] Saving precursor levels matrix
[34:20] Precursor levels matrix (1% precursor and protein group FDR) saved to out-DIANN_quantB/WU2.6.0_metox_report-first-pass.pr_matrix.tsv.
[34:20] Manifest saved to out-DIANN_quantB/WU2.6.0_metox_report-first-pass.manifest.txt
[34:20] Stats report saved to out-DIANN_quantB/WU2.6.0_metox_report-first-pass.stats.tsv
[34:20] Generating spectral library:
[34:22] 108114 target and 6158 decoy precursors saved
[34:23] Spectral library saved to out-DIANN_quantB/WU2.6.0_metox_report-lib.parquet

[34:30] Loading spectral library out-DIANN_quantB/WU2.6.0_metox_report-lib.parquet
[34:32] Spectral library loaded: 102626 protein isoforms, 102393 protein groups and 114272 precursors in 106449 elution groups (targets and decoys).
[34:32] Loading protein annotations from FASTA input/ProteoBenchFASTA_Entrapment_Human_with_contaminants_entrapment_pep.fasta
[34:55] Annotating library proteins with information from the FASTA database
[34:57] Gene names missing for some isoforms
[34:57] Library contains 102626 proteins, and 0 genes
WARNING: no gene information in the FASTA or library: consider using --ids-to-names
[34:57] Initialising library
[34:58] Saving the library to out-DIANN_quantB/WU2.6.0_metox_report-lib.parquet.skyline.speclib


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

[34:58] File #1/6
[34:58] Loading run /raw/LFQ_Astral_DIA_15min_50ng_Condition_A_REP1.raw
[35:11] Pre-processing...
[35:12] 2931 MS1 and 293271 MS2 scans in 977 (inferred) and 977 (encoded) cycles, 108114 precursors in range
[35:12] Calibrating with mass accuracies 21 (MS1), 25 (MS2)
[35:13] RT window set to 0.426204
[35:13] Recommended MS1 mass accuracy setting: 2.7 ppm
[35:13] Searching decoys
[35:14] Main search
[35:15] Removing low confidence identifications
[35:19] Removing interfering precursors
[35:20] Training neural networks on 98403 target and 44478 decoy PSMs
[35:34] Training neural networks on 98172 target and 51715 decoy PSMs
[35:48] Precursors at 1% peptidoform FDR: 76892
[35:48] Number of IDs at 0.01 FDR: 78261
[35:48] Calculating protein q-values
[35:48] Number of proteins identified at 1% FDR: 70586 (precursor-level), 70981 (protein-level) (inference performed using proteotypic peptides only)
[35:48] Quantification
[35:49] Precursors with scored PTMs at 1% FDR: 2107 out of 2184 considered
[35:49] Precursors with all scored PTM sites unoccupied at 1% FDR: 74786
[35:49] Precursors with PTMs localised (when required) with > 90% confidence: 2030 out of 2107

[35:50] File #2/6
[35:50] Loading run /raw/LFQ_Astral_DIA_15min_50ng_Condition_A_REP2.raw
[36:02] Pre-processing...
[36:03] 2933 MS1 and 293433 MS2 scans in 978 (inferred) and 978 (encoded) cycles, 108114 precursors in range
[36:03] Calibrating with mass accuracies 21 (MS1), 25 (MS2)
[36:03] RT window set to 0.382313
[36:03] Recommended MS1 mass accuracy setting: 2.5 ppm
[36:04] Searching decoys
[36:04] Main search
[36:06] Removing low confidence identifications
[36:09] Removing interfering precursors
[36:10] Training neural networks on 98433 target and 44213 decoy PSMs
[36:24] Training neural networks on 98198 target and 51698 decoy PSMs
[36:38] Precursors at 1% peptidoform FDR: 77282
[36:38] Number of IDs at 0.01 FDR: 78447
[36:38] Calculating protein q-values
[36:39] Number of proteins identified at 1% FDR: 70744 (precursor-level), 71037 (protein-level) (inference performed using proteotypic peptides only)
[36:39] Quantification
[36:40] Precursors with scored PTMs at 1% FDR: 2125 out of 2194 considered
[36:40] Precursors with all scored PTM sites unoccupied at 1% FDR: 75157
[36:40] Precursors with PTMs localised (when required) with > 90% confidence: 2058 out of 2125

[36:40] File #3/6
[36:40] Loading run /raw/LFQ_Astral_DIA_15min_50ng_Condition_A_REP3.raw
[36:52] Pre-processing...
[36:53] 2932 MS1 and 293358 MS2 scans in 978 (inferred) and 978 (encoded) cycles, 108114 precursors in range
[36:53] Calibrating with mass accuracies 21 (MS1), 25 (MS2)
[36:54] RT window set to 0.408678
[36:54] Recommended MS1 mass accuracy setting: 2.5 ppm
[36:54] Searching decoys
[36:55] Main search
[36:56] Removing low confidence identifications
[36:59] Removing interfering precursors
[37:00] Training neural networks on 98934 target and 45188 decoy PSMs
[37:14] Training neural networks on 98691 target and 52124 decoy PSMs
[37:29] Precursors at 1% peptidoform FDR: 77978
[37:29] Number of IDs at 0.01 FDR: 79475
[37:29] Calculating protein q-values
[37:29] Number of proteins identified at 1% FDR: 71595 (precursor-level), 72109 (protein-level) (inference performed using proteotypic peptides only)
[37:29] Quantification
[37:30] Precursors with scored PTMs at 1% FDR: 2138 out of 2246 considered
[37:30] Precursors with all scored PTM sites unoccupied at 1% FDR: 75840
[37:30] Precursors with PTMs localised (when required) with > 90% confidence: 2066 out of 2138

[37:30] File #4/6
[37:30] Loading run /raw/LFQ_Astral_DIA_15min_50ng_Condition_B_REP1.raw
[37:44] Pre-processing...
[37:45] 2933 MS1 and 293382 MS2 scans in 978 (inferred) and 978 (encoded) cycles, 108114 precursors in range
[37:45] Calibrating with mass accuracies 21 (MS1), 25 (MS2)
[37:45] RT window set to 0.424337
[37:45] Recommended MS1 mass accuracy setting: 2.6 ppm
[37:46] Searching decoys
[37:46] Main search
[37:48] Removing low confidence identifications
[37:51] Removing interfering precursors
[37:52] Training neural networks on 99974 target and 44587 decoy PSMs
[38:06] Training neural networks on 99743 target and 52787 decoy PSMs
[38:21] Precursors at 1% peptidoform FDR: 80080
[38:21] Number of IDs at 0.01 FDR: 81443
[38:21] Calculating protein q-values
[38:21] Number of proteins identified at 1% FDR: 73116 (precursor-level), 73746 (protein-level) (inference performed using proteotypic peptides only)
[38:21] Quantification
[38:22] Precursors with scored PTMs at 1% FDR: 2371 out of 2504 considered
[38:22] Precursors with all scored PTM sites unoccupied at 1% FDR: 77709
[38:22] Precursors with PTMs localised (when required) with > 90% confidence: 2306 out of 2371

[38:22] File #5/6
[38:22] Loading run /raw/LFQ_Astral_DIA_15min_50ng_Condition_B_REP2.raw
[38:36] Pre-processing...
[38:37] 2933 MS1 and 293330 MS2 scans in 978 (inferred) and 978 (encoded) cycles, 108114 precursors in range
[38:37] Calibrating with mass accuracies 21 (MS1), 25 (MS2)
[38:37] RT window set to 0.41981
[38:37] Recommended MS1 mass accuracy setting: 2.4 ppm
[38:38] Searching decoys
[38:38] Main search
[38:40] Removing low confidence identifications
[38:43] Removing interfering precursors
[38:44] Training neural networks on 99105 target and 44619 decoy PSMs
[38:58] Training neural networks on 98820 target and 52289 decoy PSMs
[39:12] Precursors at 1% peptidoform FDR: 79148
[39:13] Number of IDs at 0.01 FDR: 80549
[39:13] Calculating protein q-values
[39:13] Number of proteins identified at 1% FDR: 72411 (precursor-level), 72546 (protein-level) (inference performed using proteotypic peptides only)
[39:13] Quantification
[39:14] Precursors with scored PTMs at 1% FDR: 2311 out of 2432 considered
[39:14] Precursors with all scored PTM sites unoccupied at 1% FDR: 76838
[39:14] Precursors with PTMs localised (when required) with > 90% confidence: 2245 out of 2311

[39:15] File #6/6
[39:15] Loading run /raw/LFQ_Astral_DIA_15min_50ng_Condition_B_REP3.raw
[39:27] Pre-processing...
[39:27] 2934 MS1 and 293446 MS2 scans in 978 (inferred) and 978 (encoded) cycles, 108114 precursors in range
[39:27] Calibrating with mass accuracies 21 (MS1), 25 (MS2)
[39:28] RT window set to 0.438246
[39:28] Recommended MS1 mass accuracy setting: 2.4 ppm
[39:28] Searching decoys
[39:29] Main search
[39:31] Removing low confidence identifications
[39:34] Removing interfering precursors
[39:35] Training neural networks on 99836 target and 43881 decoy PSMs
[39:49] Training neural networks on 99590 target and 52128 decoy PSMs
[40:04] Precursors at 1% peptidoform FDR: 80016
[40:04] Number of IDs at 0.01 FDR: 81291
[40:04] Calculating protein q-values
[40:04] Number of proteins identified at 1% FDR: 73013 (precursor-level), 73756 (protein-level) (inference performed using proteotypic peptides only)
[40:04] Quantification
[40:05] Precursors with scored PTMs at 1% FDR: 2370 out of 2512 considered
[40:05] Precursors with all scored PTM sites unoccupied at 1% FDR: 77646
[40:05] Precursors with PTMs localised (when required) with > 90% confidence: 2295 out of 2370

[40:06] Cross-run analysis
[40:06] Reading quantification information: 6 files
[40:09] Target precursors at 1% global q-value: 86207
[40:09] Quantifying peptides
[40:57] Quantification parameters: 0.359414, 0.00164048, 0.00159488, 0.0128971, 0.267096, 0.1531, 0.172787, 0.311897, 0.308528, 0.0735013, 0.0515976, 0.0582799, 0.410933, 0.0510572, 0.0596167, 0.0115756
[41:08] Quantifying proteins
[41:08] Calculating q-values for protein and gene groups
[41:08] Calculating global q-values for protein and gene groups
[41:08] Protein groups with global q-value <= 0.01: 77438
[41:11] Compressed report saved to out-DIANN_quantB/WU2.6.0_metox_report.parquet. Use R 'arrow' or Python 'PyArrow' package to process
[41:11] Site report saved to out-DIANN_quantB/WU2.6.0_metox_report.site_report.parquet
[41:11] Saving precursor levels matrix
[41:12] Precursor levels matrix (1% precursor and protein group FDR) saved to out-DIANN_quantB/WU2.6.0_metox_report.pr_matrix.tsv.
[41:12] Saving protein group levels matrix
[41:12] Protein groups matrix saved to out-DIANN_quantB/WU2.6.0_metox_report.pg_matrix.tsv.
[41:12] Saving gene group levels matrix
[41:12] Gene groups matrix saved to out-DIANN_quantB/WU2.6.0_metox_report.gg_matrix.tsv.
[41:12] Saving unique genes levels matrix
[41:12] Unique genes matrix saved to out-DIANN_quantB/WU2.6.0_metox_report.unique_genes_matrix.tsv.
[41:12] Manifest saved to out-DIANN_quantB/WU2.6.0_metox_report.manifest.txt
[41:12] Stats report saved to out-DIANN_quantB/WU2.6.0_metox_report.stats.tsv

