
DIA-NN 2.3.2 Academia  (Data-Independent Acquisition by Neural Networks)
Compiled on Jan 22 2026 05:01:57
Current date and time: Tue Feb 17 10:59:00 2026
Logical CPU cores: 128
diann-linux --lib out-DIANN_libA/WU340864_report-lib.predicted.speclib --fasta input/p34486_Proteobench_TripleProteome_20240614.fasta --reannotate --f input/raw/LFQ_Astral_DIA_15min_50ng_Condition_B_REP3.mzML --f input/raw/LFQ_Astral_DIA_15min_50ng_Condition_A_REP2.mzML --f input/raw/LFQ_Astral_DIA_15min_50ng_Condition_B_REP2.mzML --f input/raw/LFQ_Astral_DIA_15min_50ng_Condition_A_REP1.mzML --f input/raw/LFQ_Astral_DIA_15min_50ng_Condition_B_REP1.mzML --f input/raw/LFQ_Astral_DIA_15min_50ng_Condition_A_REP3.mzML --threads 64 --qvalue 0.01 --cut K*,R* --min-pep-len 6 --max-pep-len 30 --min-pr-charge 1 --max-pr-charge 3 --min-pr-mz 400 --max-pr-mz 1500 --min-fr-mz 200 --max-fr-mz 1800 --missed-cleavages 1 --verbose 1 --var-mods 1 --var-mod UniMod:35,15.994915,M --met-excision --unimod4 --rt-profiling --matrices --pg-level 2 --relaxed-prot-inf --reanalyse --gen-spec-lib --out-lib out-DIANN_quantB/WU340864_report-lib.parquet --out out-DIANN_quantB/WU340864_report.parquet --temp temp-DIANN_quantB 

Library precursors will be reannotated using the FASTA database
Thread number set to 64
Output will be filtered at 0.01 FDR
In silico digest will involve cuts at K*,R*
Min peptide length set to 6
Max peptide length set to 30
Min precursor charge set to 1
Max precursor charge set to 3
Min precursor m/z set to 400
Max precursor m/z set to 1500
Min fragment m/z set to 200
Max fragment m/z set to 1800
Maximum number of missed cleavages set to 1
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: genes; this determines which peptides are considered 'proteotypic' and thus affects protein FDR calculation
Heuristic protein grouping will be used, to reduce the number of protein groups obtained; this mode is recommended for benchmarking protein ID numbers, GO/pathway and system-scale analyses
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/WU340864_report-lib.predicted.speclib
[0:02] Library annotated with sequence database(s): input/p34486_Proteobench_TripleProteome_20240614.fasta
[0:04] Spectral library loaded: 31838 protein isoforms, 51766 protein groups and 5279118 precursors in 2644630 elution groups.
[0:04] Loading FASTA input/p34486_Proteobench_TripleProteome_20240614.fasta
[0:25] Reannotating library precursors with information from the FASTA database
[0:30] Finding proteotypic peptides (assuming that the list of UniProt ids provided for each peptide is complete)
[0:30] 5279118 precursors generated
[0:30] Protein names missing for some isoforms
[0:30] Gene names missing for some isoforms
[0:30] Library contains 31686 proteins, and 0 genes
WARNING: no gene information in the FASTA or library: consider using --ids-to-names
[0:34] Initialising library

First pass: generating a spectral library from DIA data

[0:44] File #1/6
[0:44] Loading run input/raw/LFQ_Astral_DIA_15min_50ng_Condition_B_REP3.mzML
[0:55] Pre-processing...
[0:58] 2934 MS1 and 293446 MS2 scans in 978 (inferred) and 978 (encoded) cycles, 3618705 precursors in range
[0:58] Calibrating with mass accuracies 21 (MS1), 25 (MS2)
[1:05] RT window set to 1.09841
[1:05] Peak width: 2.924
[1:05] Scan window radius set to 6
[1:05] Recommended MS1 mass accuracy setting: 3.1 ppm
[1:11] Optimised mass accuracy: 8 ppm
[1:13] Searching decoys
[1:21] Main search
[1:39] Removing low confidence identifications
[1:51] Removing interfering precursors
[2:00] Training neural networks on 298492 target and 257223 decoy PSMs
[2:34] Training neural networks on 298492 target and 254900 decoy PSMs
[3:01] IDs at 0.01 FDR: 102775
[3:02] Precursors at 1% peptidoform FDR: 100023
[3:03] Number of IDs at 0.01 FDR: 105401
[3:03] Calculating protein q-values
[3:03] Number of genes identified at 1% FDR: 0 (precursor-level), 0 (protein-level) (inference performed using proteotypic peptides only)
[3:03] Quantification
[3:04] Precursors with scored PTMs at 1% FDR: 3038 out of 3384 considered
[3:04] Precursors with all scored PTM sites unoccupied at 1% FDR: 98015
[3:04] Precursors with PTMs localised (when required) with > 90% confidence: 2924 out of 3038
[3:05] Quantification information saved to temp-DIANN_quantB/input_raw_LFQ_Astral_DIA_15min_50ng_Condition_B_REP3_mzML.quant

[3:05] File #2/6
[3:05] Loading run input/raw/LFQ_Astral_DIA_15min_50ng_Condition_A_REP2.mzML
[3:15] Pre-processing...
[3:18] 2933 MS1 and 293433 MS2 scans in 978 (inferred) and 978 (encoded) cycles, 3618705 precursors in range
[3:18] Calibrating with mass accuracies 21 (MS1), 25 (MS2)
[3:24] RT window set to 1.1737
[3:24] Recommended MS1 mass accuracy setting: 3 ppm
[3:27] Searching decoys
[3:35] Main search
[3:52] Removing low confidence identifications
[4:04] Removing interfering precursors
[4:13] Training neural networks on 316941 target and 274722 decoy PSMs
[4:47] Training neural networks on 316941 target and 271443 decoy PSMs
[5:20] IDs at 0.01 FDR: 104029
[5:21] Precursors at 1% peptidoform FDR: 101055
[5:22] Number of IDs at 0.01 FDR: 106349
[5:22] Calculating protein q-values
[5:22] Number of genes identified at 1% FDR: 0 (precursor-level), 0 (protein-level) (inference performed using proteotypic peptides only)
[5:22] Quantification
[5:23] Precursors with scored PTMs at 1% FDR: 2433 out of 2700 considered
[5:23] Precursors with all scored PTM sites unoccupied at 1% FDR: 99443
[5:23] Precursors with PTMs localised (when required) with > 90% confidence: 2346 out of 2433
[5:24] Quantification information saved to temp-DIANN_quantB/input_raw_LFQ_Astral_DIA_15min_50ng_Condition_A_REP2_mzML.quant

[5:24] File #3/6
[5:24] Loading run input/raw/LFQ_Astral_DIA_15min_50ng_Condition_B_REP2.mzML
[5:34] Pre-processing...
[5:38] 2933 MS1 and 293330 MS2 scans in 978 (inferred) and 978 (encoded) cycles, 3618705 precursors in range
[5:38] Calibrating with mass accuracies 21 (MS1), 25 (MS2)
[5:44] RT window set to 1.19949
[5:44] Recommended MS1 mass accuracy setting: 2.8 ppm
[5:46] Searching decoys
[5:55] Main search
[6:13] Removing low confidence identifications
[6:24] Removing interfering precursors
[6:33] Training neural networks on 322015 target and 275201 decoy PSMs
[7:13] Training neural networks on 322015 target and 271424 decoy PSMs
[7:42] IDs at 0.01 FDR: 104382
[7:43] Precursors at 1% peptidoform FDR: 101786
[7:44] Number of IDs at 0.01 FDR: 107044
[7:44] Calculating protein q-values
[7:44] Number of genes identified at 1% FDR: 0 (precursor-level), 0 (protein-level) (inference performed using proteotypic peptides only)
[7:44] Quantification
[7:45] Precursors with scored PTMs at 1% FDR: 3118 out of 3393 considered
[7:45] Precursors with all scored PTM sites unoccupied at 1% FDR: 99710
[7:45] Precursors with PTMs localised (when required) with > 90% confidence: 3007 out of 3118
[7:46] Quantification information saved to temp-DIANN_quantB/input_raw_LFQ_Astral_DIA_15min_50ng_Condition_B_REP2_mzML.quant

[7:46] File #4/6
[7:46] Loading run input/raw/LFQ_Astral_DIA_15min_50ng_Condition_A_REP1.mzML
[7:57] Pre-processing...
[8:00] 2931 MS1 and 293271 MS2 scans in 977 (inferred) and 977 (encoded) cycles, 3618705 precursors in range
[8:01] Calibrating with mass accuracies 21 (MS1), 25 (MS2)
[8:15] RT window set to 1.22145
[8:15] Recommended MS1 mass accuracy setting: 2.8 ppm
[8:21] Searching decoys
[8:38] Main search
[9:08] Removing low confidence identifications
[9:33] Removing interfering precursors
[9:54] Training neural networks on 331034 target and 287821 decoy PSMs
[10:58] Training neural networks on 331034 target and 283431 decoy PSMs
[11:33] IDs at 0.01 FDR: 102077
[11:34] Precursors at 1% peptidoform FDR: 99998
[11:35] Number of IDs at 0.01 FDR: 104519
[11:35] Calculating protein q-values
[11:35] Number of genes identified at 1% FDR: 0 (precursor-level), 0 (protein-level) (inference performed using proteotypic peptides only)
[11:35] Quantification
[11:36] Precursors with scored PTMs at 1% FDR: 2411 out of 2594 considered
[11:36] Precursors with all scored PTM sites unoccupied at 1% FDR: 98727
[11:36] Precursors with PTMs localised (when required) with > 90% confidence: 2304 out of 2411
[11:37] Quantification information saved to temp-DIANN_quantB/input_raw_LFQ_Astral_DIA_15min_50ng_Condition_A_REP1_mzML.quant

[11:37] File #5/6
[11:37] Loading run input/raw/LFQ_Astral_DIA_15min_50ng_Condition_B_REP1.mzML
[11:48] Pre-processing...
[11:51] 2933 MS1 and 293382 MS2 scans in 978 (inferred) and 978 (encoded) cycles, 3618705 precursors in range
[11:51] Calibrating with mass accuracies 21 (MS1), 25 (MS2)
[11:57] RT window set to 1.11448
[11:57] Recommended MS1 mass accuracy setting: 2.8 ppm
[11:59] Searching decoys
[12:07] Main search
[12:29] Removing low confidence identifications
[12:44] Removing interfering precursors
[12:55] Training neural networks on 332750 target and 289218 decoy PSMs
[13:46] Training neural networks on 332750 target and 285178 decoy PSMs
[14:35] IDs at 0.01 FDR: 103618
[14:36] Precursors at 1% peptidoform FDR: 100985
[14:37] Number of IDs at 0.01 FDR: 106199
[14:37] Calculating protein q-values
[14:38] Number of genes identified at 1% FDR: 0 (precursor-level), 0 (protein-level) (inference performed using proteotypic peptides only)
[14:38] Quantification
[14:39] Precursors with scored PTMs at 1% FDR: 3047 out of 3331 considered
[14:39] Precursors with all scored PTM sites unoccupied at 1% FDR: 98991
[14:39] Precursors with PTMs localised (when required) with > 90% confidence: 2937 out of 3047
[14:40] Quantification information saved to temp-DIANN_quantB/input_raw_LFQ_Astral_DIA_15min_50ng_Condition_B_REP1_mzML.quant

[14:40] File #6/6
[14:40] Loading run input/raw/LFQ_Astral_DIA_15min_50ng_Condition_A_REP3.mzML
[14:56] Pre-processing...
[14:59] 2932 MS1 and 293358 MS2 scans in 978 (inferred) and 978 (encoded) cycles, 3618705 precursors in range
[14:59] Calibrating with mass accuracies 21 (MS1), 25 (MS2)
[15:07] RT window set to 1.17252
[15:07] Recommended MS1 mass accuracy setting: 2.8 ppm
[15:11] Searching decoys
[15:22] Main search
[15:46] Removing low confidence identifications
[16:01] Removing interfering precursors
[16:13] Training neural networks on 324471 target and 281767 decoy PSMs
[17:10] Training neural networks on 324471 target and 278825 decoy PSMs
[17:58] IDs at 0.01 FDR: 103983
[17:59] Precursors at 1% peptidoform FDR: 101132
[18:00] Number of IDs at 0.01 FDR: 106234
[18:00] Calculating protein q-values
[18:01] Number of genes identified at 1% FDR: 0 (precursor-level), 0 (protein-level) (inference performed using proteotypic peptides only)
[18:01] Quantification
[18:02] Precursors with scored PTMs at 1% FDR: 2388 out of 2703 considered
[18:02] Precursors with all scored PTM sites unoccupied at 1% FDR: 99621
[18:02] Precursors with PTMs localised (when required) with > 90% confidence: 2300 out of 2388
[18:02] Quantification information saved to temp-DIANN_quantB/input_raw_LFQ_Astral_DIA_15min_50ng_Condition_A_REP3_mzML.quant

[18:02] Cross-run analysis
[18:02] Reading quantification information: 6 files
[18:19] Quantifying peptides
[19:13] Assembling protein groups
[19:16] Quantifying proteins
[19:16] Calculating q-values for protein and gene groups
[19:18] Calculating global q-values for protein and gene groups
[19:18] Protein groups with global q-value <= 0.01: 11606
[19:21] Compressed report saved to out-DIANN_quantB/WU340864_report-first-pass.parquet. Use R 'arrow' or Python 'PyArrow' package to process
[19:21] Site report saved to out-DIANN_quantB/WU340864_report-first-pass.site_report.parquet
[19:21] Saving precursor levels matrix
[19:22] Precursor levels matrix (1% precursor and protein group FDR) saved to out-DIANN_quantB/WU340864_report-first-pass.pr_matrix.tsv.
[19:22] Manifest saved to out-DIANN_quantB/WU340864_report-first-pass.manifest.txt
[19:22] Stats report saved to out-DIANN_quantB/WU340864_report-first-pass.stats.tsv
[19:22] Generating spectral library:
[19:24] 135598 target and 1381 decoy precursors saved
[19:25] Spectral library saved to out-DIANN_quantB/WU340864_report-lib.parquet

[19:26] Loading spectral library out-DIANN_quantB/WU340864_report-lib.parquet
[19:27] Spectral library loaded: 12946 protein isoforms, 12795 protein groups and 136979 precursors in 128997 elution groups.
[19:27] Loading protein annotations from FASTA input/p34486_Proteobench_TripleProteome_20240614.fasta
[19:27] Annotating library proteins with information from the FASTA database
[19:27] Gene names missing for some isoforms
[19:27] Library contains 12936 proteins, and 0 genes
WARNING: no gene information in the FASTA or library: consider using --ids-to-names
[19:27] Initialising library
[19:29] Saving the library to out-DIANN_quantB/WU340864_report-lib.parquet.skyline.speclib


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

[19:29] File #1/6
[19:29] Loading run input/raw/LFQ_Astral_DIA_15min_50ng_Condition_B_REP3.mzML
[19:44] Pre-processing...
[19:44] 2934 MS1 and 293446 MS2 scans in 978 (inferred) and 978 (encoded) cycles, 135598 precursors in range
[19:44] Calibrating with mass accuracies 21 (MS1), 25 (MS2)
[19:45] RT window set to 0.447648
[19:45] Recommended MS1 mass accuracy setting: 3.1 ppm
[19:45] Searching decoys
[19:45] Main search
[19:47] Removing low confidence identifications
[19:50] Removing interfering precursors
[19:52] Training neural networks on 120633 target and 59473 decoy PSMs
[20:06] Training neural networks on 120561 target and 68024 decoy PSMs
[20:21] IDs at 0.01 FDR: 115525
[20:22] Precursors at 1% peptidoform FDR: 113479
[20:22] Number of IDs at 0.01 FDR: 116195
[20:22] Calculating protein q-values
[20:22] Number of genes identified at 1% FDR: 0 (precursor-level), 0 (protein-level) (inference performed using proteotypic peptides only)
[20:22] Quantification
[20:23] Precursors with scored PTMs at 1% FDR: 3191 out of 3280 considered
[20:23] Precursors with all scored PTM sites unoccupied at 1% FDR: 110745
[20:23] Precursors with PTMs localised (when required) with > 90% confidence: 3095 out of 3191

[20:23] File #2/6
[20:23] Loading run input/raw/LFQ_Astral_DIA_15min_50ng_Condition_A_REP2.mzML
[20:37] Pre-processing...
[20:38] 2933 MS1 and 293433 MS2 scans in 978 (inferred) and 978 (encoded) cycles, 135598 precursors in range
[20:38] Calibrating with mass accuracies 21 (MS1), 25 (MS2)
[20:38] RT window set to 0.432849
[20:38] Recommended MS1 mass accuracy setting: 3 ppm
[20:38] Searching decoys
[20:39] Main search
[20:40] Removing low confidence identifications
[20:42] Removing interfering precursors
[20:44] Training neural networks on 120170 target and 59281 decoy PSMs
[21:00] Training neural networks on 120101 target and 67235 decoy PSMs
[21:11] IDs at 0.01 FDR: 115266
[21:11] Precursors at 1% peptidoform FDR: 113688
[21:11] Number of IDs at 0.01 FDR: 115805
[21:11] Calculating protein q-values
[21:11] Number of genes identified at 1% FDR: 0 (precursor-level), 0 (protein-level) (inference performed using proteotypic peptides only)
[21:11] Quantification
[21:12] Precursors with scored PTMs at 1% FDR: 2899 out of 2971 considered
[21:12] Precursors with all scored PTM sites unoccupied at 1% FDR: 111151
[21:12] Precursors with PTMs localised (when required) with > 90% confidence: 2811 out of 2899

[21:13] File #3/6
[21:13] Loading run input/raw/LFQ_Astral_DIA_15min_50ng_Condition_B_REP2.mzML
[21:26] Pre-processing...
[21:26] 2933 MS1 and 293330 MS2 scans in 978 (inferred) and 978 (encoded) cycles, 135598 precursors in range
[21:26] Calibrating with mass accuracies 21 (MS1), 25 (MS2)
[21:27] RT window set to 0.450323
[21:27] Recommended MS1 mass accuracy setting: 3 ppm
[21:27] Searching decoys
[21:27] Main search
[21:29] Removing low confidence identifications
[21:32] Removing interfering precursors
[21:33] Training neural networks on 120941 target and 60229 decoy PSMs
[21:44] Training neural networks on 120869 target and 68454 decoy PSMs
[21:58] IDs at 0.01 FDR: 116182
[21:58] Precursors at 1% peptidoform FDR: 114088
[21:58] Number of IDs at 0.01 FDR: 116606
[21:58] Calculating protein q-values
[21:58] Number of genes identified at 1% FDR: 0 (precursor-level), 0 (protein-level) (inference performed using proteotypic peptides only)
[21:58] Quantification
[21:59] Precursors with scored PTMs at 1% FDR: 3250 out of 3295 considered
[21:59] Precursors with all scored PTM sites unoccupied at 1% FDR: 111096
[21:59] Precursors with PTMs localised (when required) with > 90% confidence: 3154 out of 3250

[22:00] File #4/6
[22:00] Loading run input/raw/LFQ_Astral_DIA_15min_50ng_Condition_A_REP1.mzML
[22:12] Pre-processing...
[22:13] 2931 MS1 and 293271 MS2 scans in 977 (inferred) and 977 (encoded) cycles, 135598 precursors in range
[22:13] Calibrating with mass accuracies 21 (MS1), 25 (MS2)
[22:13] RT window set to 0.427065
[22:13] Recommended MS1 mass accuracy setting: 3 ppm
[22:14] Searching decoys
[22:14] Main search
[22:16] Removing low confidence identifications
[22:19] Removing interfering precursors
[22:20] Training neural networks on 119678 target and 58489 decoy PSMs
[22:30] Training neural networks on 119622 target and 66647 decoy PSMs
[22:43] IDs at 0.01 FDR: 114494
[22:43] Precursors at 1% peptidoform FDR: 112414
[22:44] Number of IDs at 0.01 FDR: 115057
[22:44] Calculating protein q-values
[22:44] Number of genes identified at 1% FDR: 0 (precursor-level), 0 (protein-level) (inference performed using proteotypic peptides only)
[22:44] Quantification
[22:44] Precursors with scored PTMs at 1% FDR: 2851 out of 2937 considered
[22:44] Precursors with all scored PTM sites unoccupied at 1% FDR: 109889
[22:44] Precursors with PTMs localised (when required) with > 90% confidence: 2756 out of 2851

[22:45] File #5/6
[22:45] Loading run input/raw/LFQ_Astral_DIA_15min_50ng_Condition_B_REP1.mzML
[22:59] Pre-processing...
[22:59] 2933 MS1 and 293382 MS2 scans in 978 (inferred) and 978 (encoded) cycles, 135598 precursors in range
[23:00] Calibrating with mass accuracies 21 (MS1), 25 (MS2)
[23:00] RT window set to 0.451407
[23:00] Recommended MS1 mass accuracy setting: 2.9 ppm
[23:00] Searching decoys
[23:01] Main search
[23:02] Removing low confidence identifications
[23:06] Removing interfering precursors
[23:07] Training neural networks on 120930 target and 60408 decoy PSMs
[23:19] Training neural networks on 120869 target and 68273 decoy PSMs
[23:33] IDs at 0.01 FDR: 116357
[23:33] Precursors at 1% peptidoform FDR: 114035
[23:33] Number of IDs at 0.01 FDR: 116896
[23:33] Calculating protein q-values
[23:33] Number of genes identified at 1% FDR: 0 (precursor-level), 0 (protein-level) (inference performed using proteotypic peptides only)
[23:33] Quantification
[23:34] Precursors with scored PTMs at 1% FDR: 3229 out of 3287 considered
[23:34] Precursors with all scored PTM sites unoccupied at 1% FDR: 111140
[23:34] Precursors with PTMs localised (when required) with > 90% confidence: 3137 out of 3229

[23:34] File #6/6
[23:34] Loading run input/raw/LFQ_Astral_DIA_15min_50ng_Condition_A_REP3.mzML
[23:50] Pre-processing...
[23:50] 2932 MS1 and 293358 MS2 scans in 978 (inferred) and 978 (encoded) cycles, 135598 precursors in range
[23:50] Calibrating with mass accuracies 21 (MS1), 25 (MS2)
[23:51] RT window set to 0.428726
[23:51] Recommended MS1 mass accuracy setting: 3.1 ppm
[23:51] Searching decoys
[23:51] Main search
[23:53] Removing low confidence identifications
[23:56] Removing interfering precursors
[23:58] Training neural networks on 120110 target and 59575 decoy PSMs
[24:12] Training neural networks on 120023 target and 67600 decoy PSMs
[24:27] IDs at 0.01 FDR: 115528
[24:27] Precursors at 1% peptidoform FDR: 113416
[24:27] Number of IDs at 0.01 FDR: 115894
[24:27] Calculating protein q-values
[24:27] Number of genes identified at 1% FDR: 0 (precursor-level), 0 (protein-level) (inference performed using proteotypic peptides only)
[24:27] Quantification
[24:28] Precursors with scored PTMs at 1% FDR: 2923 out of 2987 considered
[24:28] Precursors with all scored PTM sites unoccupied at 1% FDR: 110720
[24:28] Precursors with PTMs localised (when required) with > 90% confidence: 2827 out of 2923

[24:29] Cross-run analysis
[24:29] Reading quantification information: 6 files
[24:31] Quantifying peptides
[25:24] Quantification parameters: 0.36697, 0.00137305, 0.00161066, 0.0120532, 0.012104, 0.0121095, 0.194379, 0.244344, 0.196893, 0.0134819, 0.0338743, 0.01477, 0.383382, 0.0530956, 0.0785424, 0.0120246
[25:41] Quantifying proteins
[25:41] Calculating q-values for protein and gene groups
[25:41] Calculating global q-values for protein and gene groups
[25:41] Protein groups with global q-value <= 0.01: 11037
[25:45] Compressed report saved to out-DIANN_quantB/WU340864_report.parquet. Use R 'arrow' or Python 'PyArrow' package to process
[25:45] Site report saved to out-DIANN_quantB/WU340864_report.site_report.parquet
[25:45] Saving precursor levels matrix
[25:45] Precursor levels matrix (1% precursor and protein group FDR) saved to out-DIANN_quantB/WU340864_report.pr_matrix.tsv.
[25:45] Saving protein group levels matrix
[25:45] Protein groups matrix saved to out-DIANN_quantB/WU340864_report.pg_matrix.tsv.
[25:45] Saving gene group levels matrix
[25:45] Gene groups matrix saved to out-DIANN_quantB/WU340864_report.gg_matrix.tsv.
[25:45] Saving unique genes levels matrix
[25:45] Unique genes matrix saved to out-DIANN_quantB/WU340864_report.unique_genes_matrix.tsv.
[25:45] Manifest saved to out-DIANN_quantB/WU340864_report.manifest.txt
[25:45] Stats report saved to out-DIANN_quantB/WU340864_report.stats.tsv

