
DIA-NN 2.2.0 Academia  (Data-Independent Acquisition by Neural Networks)
Compiled on May 29 2025 15:05:00
Current date and time: Fri Jul  4 10:17:51 2025
Logical CPU cores: 128
diann-2.2.0/diann-linux --f Raw_Astral/mzmls/LFQ_Astral_DIA_15min_50ng_Condition_A_REP1.mzML --f Raw_Astral/mzmls/LFQ_Astral_DIA_15min_50ng_Condition_A_REP2.mzML --f Raw_Astral/mzmls/LFQ_Astral_DIA_15min_50ng_Condition_A_REP3.mzML --f /home/robbe/ProteoBench_diaPASEF/Raw_Astral/mzmls/LFQ_Astral_DIA_15min_50ng_Condition_B_REP1.mzML --f /home/robbe/ProteoBench_diaPASEF/Raw_Astral/mzmls/LFQ_Astral_DIA_15min_50ng_Condition_B_REP2.mzML --f /home/robbe/ProteoBench_diaPASEF/Raw_Astral/mzmls/LFQ_Astral_DIA_15min_50ng_Condition_B_REP3.mzML --lib --threads 80 --verbose 1 --out run_output_Astral/diann_2.2.0_new_bounds/report.tsv --qvalue 0.01 --gen-spec-lib --predictor --fasta ProteoBenchFASTA_DDAQuantification.fasta --fasta-search --min-fr-mz 150 --max-fr-mz 2000 --met-excision --min-pep-len 6 --max-pep-len 30 --min-pr-mz 380 --max-pr-mz 980 --min-pr-charge 1 --max-pr-charge 5 --cut K*,R*,!P* --missed-cleavages 1 --unimod4 --var-mods 1 --var-mod UniMod:35,15.994915,M --var-mod UniMod:1,42.010565,*n --peptidoforms --reanalyse --relaxed-prot-inf --rt-profiling 

Thread number set to 80
Output will be filtered at 0.01 FDR
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
Min fragment m/z set to 150
Max fragment m/z set to 2000
N-terminal methionine excision enabled
Min peptide length set to 6
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 1
Max precursor charge set to 5
In silico digest will involve cuts at K*,R*
But excluding cuts at P*
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
Modification UniMod:1 with mass delta 42.0106 at *n will be considered as variable
Peptidoform scoring enabled
MBR enabled; .quant files will only be saved to disk during the first pass
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
The spectral library (if generated) will retain the original spectra but will include empirically-aligned RTs
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 UniMod:1 

6 files will be processed
[0:00] Loading FASTA ProteoBenchFASTA_DDAQuantification.fasta
[0:04] Processing FASTA
[0:06] Assembling elution groups
[0:11] 5275217 precursors generated
[0:11] Protein names missing for some isoforms
[0:11] Gene names missing for some isoforms
[0:11] Library contains 31685 proteins, and 0 genes
[0:17] [0:27] [1:58] [2:12] [2:19] [2:22] Saving the library to run_output_Astral/diann_2.2.0_new_bounds/report-lib.predicted.speclib
[2:26] Initialising library
[2:35] Loading spectral library run_output_Astral/diann_2.2.0_new_bounds/report-lib.predicted.speclib
[2:38] Library annotated with sequence database(s): ProteoBenchFASTA_DDAQuantification.fasta
[2:39] Spectral library loaded: 31837 protein isoforms, 51765 protein groups and 5275217 precursors in 2716671 elution groups.
[2:39] Loading protein annotations from FASTA ProteoBenchFASTA_DDAQuantification.fasta
[2:39] Annotating library proteins with information from the FASTA database
[2:39] Protein names missing for some isoforms
[2:39] Gene names missing for some isoforms
[2:39] Library contains 31685 proteins, and 0 genes
[2:42] Initialising library

First pass: generating a spectral library from DIA data

[2:52] File #1/6
[2:52] Loading run Raw_Astral/mzmls/LFQ_Astral_DIA_15min_50ng_Condition_A_REP1.mzML
[3:05] Pre-processing...
[3:06] 2931 MS1 and 293271 MS2 scans in 977 (inferred) and 977 (encoded) cycles, 5270221 precursors in range
[3:06] Calibrating with mass accuracies 21 (MS1), 25 (MS2)
[3:14] RT window set to 1.13803
[3:14] Peak width: 2.796
[3:14] Scan window radius set to 6
[3:14] Recommended MS1 mass accuracy setting: 2.6 ppm
[3:20] Optimised mass accuracy: 7 ppm
[3:22] Searching decoys
[3:32] Main search
[3:53] Removing low confidence identifications
[4:02] Removing interfering precursors
[4:07] Training neural networks on 216697 target and 134144 decoy PSMs
[4:29] Training neural networks on 216697 target and 136020 decoy PSMs
[4:46] Number of IDs at 0.01 FDR: 106207
[4:46] Precursors at 1% peptidoform FDR: 103425
[4:47] Calculating protein q-values
[4:48] Number of genes identified at 1% FDR: 0 (precursor-level), 0 (protein-level) (inference performed using proteotypic peptides only)
[4:48] Quantification
[4:48] Precursors with scored PTMs at 1% FDR: 3387 out of 3617 considered
[4:48] Precursors with all scored PTM sites unoccupied at 1% FDR: 100038
[4:48] Precursors with PTMs localised (when required) with > 90% confidence: 3293 out of 3387
[4:49] Quantification information saved to Raw_Astral/mzmls/LFQ_Astral_DIA_15min_50ng_Condition_A_REP1.mzML.quant

[4:49] File #2/6
[4:49] Loading run Raw_Astral/mzmls/LFQ_Astral_DIA_15min_50ng_Condition_A_REP2.mzML
[5:02] Pre-processing...
[5:03] 2933 MS1 and 293433 MS2 scans in 978 (inferred) and 978 (encoded) cycles, 5270221 precursors in range
[5:03] Calibrating with mass accuracies 21 (MS1), 25 (MS2)
[5:10] RT window set to 1.40206
[5:10] Recommended MS1 mass accuracy setting: 2.7 ppm
[5:13] Searching decoys
[5:24] Main search
[5:46] Removing low confidence identifications
[5:55] Removing interfering precursors
[6:01] Training neural networks on 222918 target and 137162 decoy PSMs
[6:22] Training neural networks on 222918 target and 139315 decoy PSMs
[6:40] Number of IDs at 0.01 FDR: 109110
[6:41] Precursors at 1% peptidoform FDR: 106577
[6:41] Calculating protein q-values
[6:42] Number of genes identified at 1% FDR: 0 (precursor-level), 0 (protein-level) (inference performed using proteotypic peptides only)
[6:42] Quantification
[6:43] Precursors with scored PTMs at 1% FDR: 3569 out of 3848 considered
[6:43] Precursors with all scored PTM sites unoccupied at 1% FDR: 103008
[6:43] Precursors with PTMs localised (when required) with > 90% confidence: 3480 out of 3569
[6:44] Quantification information saved to Raw_Astral/mzmls/LFQ_Astral_DIA_15min_50ng_Condition_A_REP2.mzML.quant

[6:44] File #3/6
[6:44] Loading run Raw_Astral/mzmls/LFQ_Astral_DIA_15min_50ng_Condition_A_REP3.mzML
[6:56] Pre-processing...
[6:57] 2932 MS1 and 293358 MS2 scans in 978 (inferred) and 978 (encoded) cycles, 5270221 precursors in range
[6:57] Calibrating with mass accuracies 21 (MS1), 25 (MS2)
[7:05] RT window set to 1.2243
[7:05] Recommended MS1 mass accuracy setting: 2.6 ppm
[7:07] Searching decoys
[7:17] Main search
[7:37] Removing low confidence identifications
[7:45] Removing interfering precursors
[7:51] Training neural networks on 221971 target and 136587 decoy PSMs
[8:12] Training neural networks on 221971 target and 138621 decoy PSMs
[8:30] Number of IDs at 0.01 FDR: 109704
[8:31] Precursors at 1% peptidoform FDR: 106547
[8:31] Calculating protein q-values
[8:32] Number of genes identified at 1% FDR: 0 (precursor-level), 0 (protein-level) (inference performed using proteotypic peptides only)
[8:32] Quantification
[8:32] Precursors with scored PTMs at 1% FDR: 3594 out of 3845 considered
[8:32] Precursors with all scored PTM sites unoccupied at 1% FDR: 102953
[8:32] Precursors with PTMs localised (when required) with > 90% confidence: 3499 out of 3594
[8:33] Quantification information saved to Raw_Astral/mzmls/LFQ_Astral_DIA_15min_50ng_Condition_A_REP3.mzML.quant

[8:33] File #4/6
[8:33] Loading run /home/robbe/ProteoBench_diaPASEF/Raw_Astral/mzmls/LFQ_Astral_DIA_15min_50ng_Condition_B_REP1.mzML
[8:46] Pre-processing...
[8:47] 2933 MS1 and 293382 MS2 scans in 978 (inferred) and 978 (encoded) cycles, 5270221 precursors in range
[8:47] Calibrating with mass accuracies 21 (MS1), 25 (MS2)
[8:53] RT window set to 1.31803
[8:53] Recommended MS1 mass accuracy setting: 2.8 ppm
[8:56] Searching decoys
[9:06] Main search
[9:28] Removing low confidence identifications
[9:37] Removing interfering precursors
[9:42] Training neural networks on 226501 target and 142620 decoy PSMs
[10:08] Training neural networks on 226501 target and 143573 decoy PSMs
[10:29] Number of IDs at 0.01 FDR: 109357
[10:29] Precursors at 1% peptidoform FDR: 105950
[10:30] Calculating protein q-values
[10:30] Number of genes identified at 1% FDR: 0 (precursor-level), 0 (protein-level) (inference performed using proteotypic peptides only)
[10:30] Quantification
[10:31] Precursors with scored PTMs at 1% FDR: 4087 out of 4452 considered
[10:31] Precursors with all scored PTM sites unoccupied at 1% FDR: 101863
[10:31] Precursors with PTMs localised (when required) with > 90% confidence: 3974 out of 4087
[10:32] Quantification information saved to /home/robbe/ProteoBench_diaPASEF/Raw_Astral/mzmls/LFQ_Astral_DIA_15min_50ng_Condition_B_REP1.mzML.quant

[10:32] File #5/6
[10:32] Loading run /home/robbe/ProteoBench_diaPASEF/Raw_Astral/mzmls/LFQ_Astral_DIA_15min_50ng_Condition_B_REP2.mzML
[10:44] Pre-processing...
[10:45] 2933 MS1 and 293330 MS2 scans in 978 (inferred) and 978 (encoded) cycles, 5270221 precursors in range
[10:46] Calibrating with mass accuracies 21 (MS1), 25 (MS2)
[10:51] RT window set to 1.27856
[10:52] Recommended MS1 mass accuracy setting: 2.7 ppm
[10:54] Searching decoys
[11:04] Main search
[11:26] Removing low confidence identifications
[11:34] Removing interfering precursors
[11:39] Training neural networks on 224270 target and 140504 decoy PSMs
[12:04] Training neural networks on 224270 target and 141820 decoy PSMs
[12:23] Number of IDs at 0.01 FDR: 109404
[12:23] Precursors at 1% peptidoform FDR: 106351
[12:24] Calculating protein q-values
[12:24] Number of genes identified at 1% FDR: 0 (precursor-level), 0 (protein-level) (inference performed using proteotypic peptides only)
[12:24] Quantification
[12:25] Precursors with scored PTMs at 1% FDR: 4127 out of 4465 considered
[12:25] Precursors with all scored PTM sites unoccupied at 1% FDR: 102224
[12:25] Precursors with PTMs localised (when required) with > 90% confidence: 4026 out of 4127
[12:26] Quantification information saved to /home/robbe/ProteoBench_diaPASEF/Raw_Astral/mzmls/LFQ_Astral_DIA_15min_50ng_Condition_B_REP2.mzML.quant

[12:26] File #6/6
[12:26] Loading run /home/robbe/ProteoBench_diaPASEF/Raw_Astral/mzmls/LFQ_Astral_DIA_15min_50ng_Condition_B_REP3.mzML
[12:38] Pre-processing...
[12:39] 2934 MS1 and 293446 MS2 scans in 978 (inferred) and 978 (encoded) cycles, 5270221 precursors in range
[12:40] Calibrating with mass accuracies 21 (MS1), 25 (MS2)
[12:45] RT window set to 1.31575
[12:46] Recommended MS1 mass accuracy setting: 2.8 ppm
[12:48] Searching decoys
[12:59] Main search
[13:21] Removing low confidence identifications
[13:29] Removing interfering precursors
[13:35] Training neural networks on 222835 target and 138651 decoy PSMs
[13:59] Training neural networks on 222835 target and 140137 decoy PSMs
[14:17] Number of IDs at 0.01 FDR: 109880
[14:17] Precursors at 1% peptidoform FDR: 107051
[14:18] Calculating protein q-values
[14:18] Number of genes identified at 1% FDR: 0 (precursor-level), 0 (protein-level) (inference performed using proteotypic peptides only)
[14:18] Quantification
[14:19] Precursors with scored PTMs at 1% FDR: 4226 out of 4483 considered
[14:19] Precursors with all scored PTM sites unoccupied at 1% FDR: 102825
[14:19] Precursors with PTMs localised (when required) with > 90% confidence: 4098 out of 4226
[14:20] Quantification information saved to /home/robbe/ProteoBench_diaPASEF/Raw_Astral/mzmls/LFQ_Astral_DIA_15min_50ng_Condition_B_REP3.mzML.quant

[14:20] Cross-run analysis
[14:20] Reading quantification information: 6 files
[14:33] Quantifying peptides
[16:59] Assembling protein groups
[17:01] Quantifying proteins
[17:01] Calculating q-values for protein and gene groups
[17:02] Calculating global q-values for protein and gene groups
[17:02] Protein groups with global q-value <= 0.01: 11551
[17:05] Compressed report saved to run_output_Astral/diann_2.2.0_new_bounds/report-first-pass.parquet. Use R 'arrow' or Python 'PyArrow' package to process
[17:05] Stats report saved to run_output_Astral/diann_2.2.0_new_bounds/report-first-pass.stats.tsv
[17:05] Generating spectral library:
[17:07] 139678 target and 1423 decoy precursors saved
[17:08] Spectral library saved to run_output_Astral/diann_2.2.0_new_bounds/report-lib.parquet

[17:09] Loading spectral library run_output_Astral/diann_2.2.0_new_bounds/report-lib.parquet
[17:11] Spectral library loaded: 13420 protein isoforms, 13271 protein groups and 141101 precursors in 131428 elution groups.
[17:11] Loading protein annotations from FASTA ProteoBenchFASTA_DDAQuantification.fasta
[17:11] Annotating library proteins with information from the FASTA database
[17:11] Gene names missing for some isoforms
[17:11] Library contains 13409 proteins, and 0 genes
[17:11] Initialising library
[17:12] Saving the library to run_output_Astral/diann_2.2.0_new_bounds/report-lib.parquet.skyline.speclib


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

[17:12] File #1/6
[17:12] Loading run Raw_Astral/mzmls/LFQ_Astral_DIA_15min_50ng_Condition_A_REP1.mzML
[17:22] Pre-processing...
[17:23] 2931 MS1 and 293271 MS2 scans in 977 (inferred) and 977 (encoded) cycles, 139678 precursors in range
[17:23] Calibrating with mass accuracies 21 (MS1), 25 (MS2)
[17:23] RT window set to 0.435041
[17:23] Recommended MS1 mass accuracy setting: 3.2 ppm
[17:24] Searching decoys
[17:24] Main search
[17:25] Removing low confidence identifications
[17:27] Removing interfering precursors
[17:29] Training neural networks on 122478 target and 59881 decoy PSMs
[17:39] Training neural networks on 122439 target and 66526 decoy PSMs
[17:48] Number of IDs at 0.01 FDR: 118667
[17:48] Precursors at 1% peptidoform FDR: 116606
[17:48] Calculating protein q-values
[17:48] Number of genes identified at 1% FDR: 0 (precursor-level), 0 (protein-level) (inference performed using proteotypic peptides only)
[17:48] Quantification
[17:49] Precursors with scored PTMs at 1% FDR: 4052 out of 4172 considered
[17:49] Precursors with all scored PTM sites unoccupied at 1% FDR: 112554
[17:49] Precursors with PTMs localised (when required) with > 90% confidence: 3954 out of 4052

[17:49] File #2/6
[17:49] Loading run Raw_Astral/mzmls/LFQ_Astral_DIA_15min_50ng_Condition_A_REP2.mzML
[17:59] Pre-processing...
[18:00] 2933 MS1 and 293433 MS2 scans in 978 (inferred) and 978 (encoded) cycles, 139678 precursors in range
[18:00] Calibrating with mass accuracies 21 (MS1), 25 (MS2)
[18:00] RT window set to 0.438037
[18:00] Recommended MS1 mass accuracy setting: 3 ppm
[18:00] Searching decoys
[18:01] Main search
[18:02] Removing low confidence identifications
[18:04] Removing interfering precursors
[18:05] Training neural networks on 122732 target and 60136 decoy PSMs
[18:15] Training neural networks on 122680 target and 66779 decoy PSMs
[18:25] Number of IDs at 0.01 FDR: 119518
[18:25] Precursors at 1% peptidoform FDR: 117832
[18:25] Calculating protein q-values
[18:25] Number of genes identified at 1% FDR: 0 (precursor-level), 0 (protein-level) (inference performed using proteotypic peptides only)
[18:25] Quantification
[18:26] Precursors with scored PTMs at 1% FDR: 4158 out of 4255 considered
[18:26] Precursors with all scored PTM sites unoccupied at 1% FDR: 113674
[18:26] Precursors with PTMs localised (when required) with > 90% confidence: 4060 out of 4158

[18:26] File #3/6
[18:26] Loading run Raw_Astral/mzmls/LFQ_Astral_DIA_15min_50ng_Condition_A_REP3.mzML
[18:37] Pre-processing...
[18:37] 2932 MS1 and 293358 MS2 scans in 978 (inferred) and 978 (encoded) cycles, 139678 precursors in range
[18:37] Calibrating with mass accuracies 21 (MS1), 25 (MS2)
[18:38] RT window set to 0.434713
[18:38] Recommended MS1 mass accuracy setting: 3.1 ppm
[18:38] Searching decoys
[18:38] Main search
[18:39] Removing low confidence identifications
[18:42] Removing interfering precursors
[18:43] Training neural networks on 122640 target and 60134 decoy PSMs
[18:52] Training neural networks on 122580 target and 66805 decoy PSMs
[19:00] Number of IDs at 0.01 FDR: 120125
[19:00] Precursors at 1% peptidoform FDR: 118646
[19:00] Calculating protein q-values
[19:00] Number of genes identified at 1% FDR: 0 (precursor-level), 0 (protein-level) (inference performed using proteotypic peptides only)
[19:00] Quantification
[19:01] Precursors with scored PTMs at 1% FDR: 4203 out of 4279 considered
[19:01] Precursors with all scored PTM sites unoccupied at 1% FDR: 114443
[19:01] Precursors with PTMs localised (when required) with > 90% confidence: 4105 out of 4203

[19:02] File #4/6
[19:02] Loading run /home/robbe/ProteoBench_diaPASEF/Raw_Astral/mzmls/LFQ_Astral_DIA_15min_50ng_Condition_B_REP1.mzML
[19:12] Pre-processing...
[19:13] 2933 MS1 and 293382 MS2 scans in 978 (inferred) and 978 (encoded) cycles, 139678 precursors in range
[19:13] Calibrating with mass accuracies 21 (MS1), 25 (MS2)
[19:13] RT window set to 0.454148
[19:13] Recommended MS1 mass accuracy setting: 3.3 ppm
[19:13] Searching decoys
[19:13] Main search
[19:14] Removing low confidence identifications
[19:17] Removing interfering precursors
[19:18] Training neural networks on 123442 target and 61292 decoy PSMs
[19:29] Training neural networks on 123395 target and 67836 decoy PSMs
[19:38] Number of IDs at 0.01 FDR: 120542
[19:38] Precursors at 1% peptidoform FDR: 118541
[19:38] Calculating protein q-values
[19:38] Number of genes identified at 1% FDR: 0 (precursor-level), 0 (protein-level) (inference performed using proteotypic peptides only)
[19:38] Quantification
[19:39] Precursors with scored PTMs at 1% FDR: 4457 out of 4567 considered
[19:39] Precursors with all scored PTM sites unoccupied at 1% FDR: 114084
[19:39] Precursors with PTMs localised (when required) with > 90% confidence: 4358 out of 4457

[19:40] File #5/6
[19:40] Loading run /home/robbe/ProteoBench_diaPASEF/Raw_Astral/mzmls/LFQ_Astral_DIA_15min_50ng_Condition_B_REP2.mzML
[19:50] Pre-processing...
[19:51] 2933 MS1 and 293330 MS2 scans in 978 (inferred) and 978 (encoded) cycles, 139678 precursors in range
[19:51] Calibrating with mass accuracies 21 (MS1), 25 (MS2)
[19:51] RT window set to 0.458103
[19:51] Recommended MS1 mass accuracy setting: 3.1 ppm
[19:51] Searching decoys
[19:52] Main search
[19:53] Removing low confidence identifications
[19:55] Removing interfering precursors
[19:56] Training neural networks on 123402 target and 61155 decoy PSMs
[20:07] Training neural networks on 123342 target and 67922 decoy PSMs
[20:16] Number of IDs at 0.01 FDR: 120655
[20:16] Precursors at 1% peptidoform FDR: 118868
[20:16] Calculating protein q-values
[20:16] Number of genes identified at 1% FDR: 0 (precursor-level), 0 (protein-level) (inference performed using proteotypic peptides only)
[20:16] Quantification
[20:17] Precursors with scored PTMs at 1% FDR: 4507 out of 4583 considered
[20:17] Precursors with all scored PTM sites unoccupied at 1% FDR: 114361
[20:17] Precursors with PTMs localised (when required) with > 90% confidence: 4402 out of 4507

[20:17] File #6/6
[20:17] Loading run /home/robbe/ProteoBench_diaPASEF/Raw_Astral/mzmls/LFQ_Astral_DIA_15min_50ng_Condition_B_REP3.mzML
[20:27] Pre-processing...
[20:28] 2934 MS1 and 293446 MS2 scans in 978 (inferred) and 978 (encoded) cycles, 139678 precursors in range
[20:28] Calibrating with mass accuracies 21 (MS1), 25 (MS2)
[20:28] RT window set to 0.451724
[20:28] Recommended MS1 mass accuracy setting: 3 ppm
[20:29] Searching decoys
[20:29] Main search
[20:30] Removing low confidence identifications
[20:33] Removing interfering precursors
[20:34] Training neural networks on 123207 target and 60645 decoy PSMs
[20:45] Training neural networks on 123154 target and 67290 decoy PSMs
[20:55] Number of IDs at 0.01 FDR: 120719
[20:55] Precursors at 1% peptidoform FDR: 118552
[20:56] Calculating protein q-values
[20:56] Number of genes identified at 1% FDR: 0 (precursor-level), 0 (protein-level) (inference performed using proteotypic peptides only)
[20:56] Quantification
[20:56] Precursors with scored PTMs at 1% FDR: 4488 out of 4582 considered
[20:56] Precursors with all scored PTM sites unoccupied at 1% FDR: 114064
[20:56] Precursors with PTMs localised (when required) with > 90% confidence: 4392 out of 4488

[20:57] Cross-run analysis
[20:57] Reading quantification information: 6 files
[20:59] Quantifying peptides
[22:56] Quantification parameters: 0.365494, 0.00134577, 0.00160095, 0.0120253, 0.0121483, 0.0121405, 0.177829, 0.244509, 0.191971, 0.0134093, 0.0361226, 0.0146007, 0.365522, 0.0526583, 0.0759812, 0.0115501
[24:04] Quantifying proteins
[24:05] Calculating q-values for protein and gene groups
[24:05] Calculating global q-values for protein and gene groups
[24:05] Protein groups with global q-value <= 0.01: 11139
[24:08] Compressed report saved to run_output_Astral/diann_2.2.0_new_bounds/report.parquet. Use R 'arrow' or Python 'PyArrow' package to process
[24:08] Stats report saved to run_output_Astral/diann_2.2.0_new_bounds/report.stats.tsv

