
DIA-NN 2.3.0 Academia  (Data-Independent Acquisition by Neural Networks)
Compiled on Sep 26 2025 02:56:25
Current date and time: Fri Sep 26 12:46:41 2025
Logical CPU cores: 128
diann-2.3.0/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_2.3.0/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*,P*,R*
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
[0:06] Processing FASTA
[0:09] Assembling elution groups
[0:16] 6821208 precursors generated
[0:16] Protein names missing for some isoforms
[0:16] Gene names missing for some isoforms
[0:16] Library contains 31721 proteins, and 0 genes
[0:22] [0:39] [2:48] [3:07] [3:10] [3:14] Saving the library to run_output_Astral/diann_2.3.0/report-lib.predicted.speclib
[3:20] Initialising library
[3:33] Loading spectral library run_output_Astral/diann_2.3.0/report-lib.predicted.speclib
[3:36] Library annotated with sequence database(s): ProteoBenchFASTA_DDAQuantification.fasta
[3:37] Spectral library loaded: 31874 protein isoforms, 67732 protein groups and 6821208 precursors in 3572561 elution groups.
[3:37] Loading protein annotations from FASTA ProteoBenchFASTA_DDAQuantification.fasta
[3:37] Annotating library proteins with information from the FASTA database
[3:37] Protein names missing for some isoforms
[3:37] Gene names missing for some isoforms
[3:37] Library contains 31721 proteins, and 0 genes
[3:42] Initialising library

First pass: generating a spectral library from DIA data

[3:55] File #1/6
[3:55] Loading run Raw_Astral/mzmls/LFQ_Astral_DIA_15min_50ng_Condition_A_REP1.mzML
[4:08] Pre-processing...
[4:10] 2931 MS1 and 293271 MS2 scans in 977 (inferred) and 977 (encoded) cycles, 6813121 precursors in range
[4:11] Calibrating with mass accuracies 21 (MS1), 25 (MS2)
[4:25] RT window set to 1.1827
[4:25] Peak width: 2.684
[4:25] Scan window radius set to 5
[4:25] Recommended MS1 mass accuracy setting: 2.5 ppm
[4:35] Optimised mass accuracy: 4.6 ppm
[4:40] Searching decoys
[4:55] Main search
[5:23] Removing low confidence identifications
[5:34] Removing interfering precursors
[5:42] Training neural networks on 185576 target and 112213 decoy PSMs
[6:09] Training neural networks on 185576 target and 114790 decoy PSMs
[6:29] IDs at 0.01 FDR: 79078
[6:29] Precursors at 1% peptidoform FDR: 77359
[6:31] Number of IDs at 0.01 FDR: 85105
[6:31] Calculating protein q-values
[6:31] Number of genes identified at 1% FDR: 0 (precursor-level), 0 (protein-level) (inference performed using proteotypic peptides only)
[6:31] Quantification
[6:32] Precursors with scored PTMs at 1% FDR: 2607 out of 2909 considered
[6:32] Precursors with all scored PTM sites unoccupied at 1% FDR: 77213
[6:32] Precursors with PTMs localised (when required) with > 90% confidence: 2520 out of 2607
[6:33] Quantification information saved to Raw_Astral/mzmls/LFQ_Astral_DIA_15min_50ng_Condition_A_REP1.mzML.quant

[6:34] File #2/6
[6:34] Loading run Raw_Astral/mzmls/LFQ_Astral_DIA_15min_50ng_Condition_A_REP2.mzML
[6:47] Pre-processing...
[6:48] 2933 MS1 and 293433 MS2 scans in 978 (inferred) and 978 (encoded) cycles, 6813121 precursors in range
[6:49] Calibrating with mass accuracies 21 (MS1), 25 (MS2)
[7:02] RT window set to 1.24511
[7:02] Recommended MS1 mass accuracy setting: 2.7 ppm
[7:06] Searching decoys
[7:23] Main search
[7:49] Removing low confidence identifications
[7:58] Removing interfering precursors
[8:06] Training neural networks on 182975 target and 109847 decoy PSMs
[8:30] Training neural networks on 182975 target and 112385 decoy PSMs
[8:49] IDs at 0.01 FDR: 80516
[8:50] Precursors at 1% peptidoform FDR: 78054
[8:51] Number of IDs at 0.01 FDR: 86181
[8:51] Calculating protein q-values
[8:51] Number of genes identified at 1% FDR: 0 (precursor-level), 0 (protein-level) (inference performed using proteotypic peptides only)
[8:51] Quantification
[8:52] Precursors with scored PTMs at 1% FDR: 2587 out of 2990 considered
[8:52] Precursors with all scored PTM sites unoccupied at 1% FDR: 77291
[8:52] Precursors with PTMs localised (when required) with > 90% confidence: 2519 out of 2587
[8:53] Quantification information saved to Raw_Astral/mzmls/LFQ_Astral_DIA_15min_50ng_Condition_A_REP2.mzML.quant

[8:53] File #3/6
[8:53] Loading run Raw_Astral/mzmls/LFQ_Astral_DIA_15min_50ng_Condition_A_REP3.mzML
[9:07] Pre-processing...
[9:08] 2932 MS1 and 293358 MS2 scans in 978 (inferred) and 978 (encoded) cycles, 6813121 precursors in range
[9:09] Calibrating with mass accuracies 21 (MS1), 25 (MS2)
[9:23] RT window set to 1.12751
[9:23] Recommended MS1 mass accuracy setting: 2.5 ppm
[9:30] Searching decoys
[9:43] Main search
[10:07] Removing low confidence identifications
[10:15] Removing interfering precursors
[10:22] Training neural networks on 186000 target and 112659 decoy PSMs
[10:49] Training neural networks on 186000 target and 115349 decoy PSMs
[11:07] IDs at 0.01 FDR: 80320
[11:07] Precursors at 1% peptidoform FDR: 77946
[11:08] Number of IDs at 0.01 FDR: 86153
[11:08] Calculating protein q-values
[11:09] Number of genes identified at 1% FDR: 0 (precursor-level), 0 (protein-level) (inference performed using proteotypic peptides only)
[11:09] Quantification
[11:10] Precursors with scored PTMs at 1% FDR: 2646 out of 2995 considered
[11:10] Precursors with all scored PTM sites unoccupied at 1% FDR: 77167
[11:10] Precursors with PTMs localised (when required) with > 90% confidence: 2575 out of 2646
[11:11] Quantification information saved to Raw_Astral/mzmls/LFQ_Astral_DIA_15min_50ng_Condition_A_REP3.mzML.quant

[11:11] File #4/6
[11:11] Loading run /home/robbe/ProteoBench_diaPASEF/Raw_Astral/mzmls/LFQ_Astral_DIA_15min_50ng_Condition_B_REP1.mzML
[11:23] Pre-processing...
[11:25] 2933 MS1 and 293382 MS2 scans in 978 (inferred) and 978 (encoded) cycles, 6813121 precursors in range
[11:25] Calibrating with mass accuracies 22 (MS1), 25 (MS2)
[11:40] RT window set to 1.29047
[11:40] Recommended MS1 mass accuracy setting: 2.7 ppm
[11:46] Searching decoys
[12:00] Main search
[12:28] Removing low confidence identifications
[12:37] Removing interfering precursors
[12:43] Training neural networks on 179452 target and 108234 decoy PSMs
[13:08] Training neural networks on 179452 target and 110817 decoy PSMs
[13:26] IDs at 0.01 FDR: 80241
[13:27] Precursors at 1% peptidoform FDR: 77524
[13:28] Number of IDs at 0.01 FDR: 85967
[13:28] Calculating protein q-values
[13:28] Number of genes identified at 1% FDR: 0 (precursor-level), 0 (protein-level) (inference performed using proteotypic peptides only)
[13:28] Quantification
[13:29] Precursors with scored PTMs at 1% FDR: 3088 out of 3482 considered
[13:29] Precursors with all scored PTM sites unoccupied at 1% FDR: 76217
[13:29] Precursors with PTMs localised (when required) with > 90% confidence: 3009 out of 3088
[13:30] Quantification information saved to /home/robbe/ProteoBench_diaPASEF/Raw_Astral/mzmls/LFQ_Astral_DIA_15min_50ng_Condition_B_REP1.mzML.quant

[13:30] File #5/6
[13:30] Loading run /home/robbe/ProteoBench_diaPASEF/Raw_Astral/mzmls/LFQ_Astral_DIA_15min_50ng_Condition_B_REP2.mzML
[13:43] Pre-processing...
[13:45] 2933 MS1 and 293330 MS2 scans in 978 (inferred) and 978 (encoded) cycles, 6813121 precursors in range
[13:45] Calibrating with mass accuracies 21 (MS1), 25 (MS2)
[13:58] RT window set to 1.22655
[13:58] Recommended MS1 mass accuracy setting: 3 ppm
[14:03] Searching decoys
[14:15] Main search
[14:44] Removing low confidence identifications
[14:52] Removing interfering precursors
[14:59] Training neural networks on 181491 target and 110413 decoy PSMs
[15:23] Training neural networks on 181491 target and 113080 decoy PSMs
[15:42] IDs at 0.01 FDR: 79623
[15:42] Precursors at 1% peptidoform FDR: 77742
[15:44] Number of IDs at 0.01 FDR: 86131
[15:44] Calculating protein q-values
[15:45] Number of genes identified at 1% FDR: 0 (precursor-level), 0 (protein-level) (inference performed using proteotypic peptides only)
[15:45] Quantification
[15:46] Precursors with scored PTMs at 1% FDR: 3099 out of 3562 considered
[15:46] Precursors with all scored PTM sites unoccupied at 1% FDR: 77084
[15:46] Precursors with PTMs localised (when required) with > 90% confidence: 3020 out of 3099
[15:48] Quantification information saved to /home/robbe/ProteoBench_diaPASEF/Raw_Astral/mzmls/LFQ_Astral_DIA_15min_50ng_Condition_B_REP2.mzML.quant

[15:48] File #6/6
[15:48] Loading run /home/robbe/ProteoBench_diaPASEF/Raw_Astral/mzmls/LFQ_Astral_DIA_15min_50ng_Condition_B_REP3.mzML
[16:01] Pre-processing...
[16:02] 2934 MS1 and 293446 MS2 scans in 978 (inferred) and 978 (encoded) cycles, 6813121 precursors in range
[16:02] Calibrating with mass accuracies 21 (MS1), 25 (MS2)
[16:16] RT window set to 1.1962
[16:17] Recommended MS1 mass accuracy setting: 2.5 ppm
[16:22] Searching decoys
[16:34] Main search
[17:02] Removing low confidence identifications
[17:11] Removing interfering precursors
[17:17] Training neural networks on 183250 target and 111519 decoy PSMs
[17:41] Training neural networks on 183250 target and 114751 decoy PSMs
[18:01] IDs at 0.01 FDR: 80986
[18:01] Precursors at 1% peptidoform FDR: 78379
[18:04] Number of IDs at 0.01 FDR: 86638
[18:04] Calculating protein q-values
[18:04] Number of genes identified at 1% FDR: 0 (precursor-level), 0 (protein-level) (inference performed using proteotypic peptides only)
[18:04] Quantification
[18:05] Precursors with scored PTMs at 1% FDR: 3158 out of 3576 considered
[18:05] Precursors with all scored PTM sites unoccupied at 1% FDR: 76923
[18:05] Precursors with PTMs localised (when required) with > 90% confidence: 3068 out of 3158
[18:06] Quantification information saved to /home/robbe/ProteoBench_diaPASEF/Raw_Astral/mzmls/LFQ_Astral_DIA_15min_50ng_Condition_B_REP3.mzML.quant

[18:06] Cross-run analysis
[18:06] Reading quantification information: 6 files
[18:21] Quantifying peptides
[18:47] Assembling protein groups
[18:49] Quantifying proteins
[18:50] Calculating q-values for protein and gene groups
[18:51] Calculating global q-values for protein and gene groups
[18:51] Protein groups with global q-value <= 0.01: 10740
[18:53] Compressed report saved to run_output_Astral/diann_2.3.0/report-first-pass.parquet. Use R 'arrow' or Python 'PyArrow' package to process
[18:53] Stats report saved to run_output_Astral/diann_2.3.0/report-first-pass.stats.tsv
[18:54] Generating spectral library:
[18:55] 105715 target and 1075 decoy precursors saved
[18:56] Spectral library saved to run_output_Astral/diann_2.3.0/report-lib.parquet

[18:57] Loading spectral library run_output_Astral/diann_2.3.0/report-lib.parquet
[18:58] Spectral library loaded: 11963 protein isoforms, 11799 protein groups and 106790 precursors in 100183 elution groups.
[18:58] Loading protein annotations from FASTA ProteoBenchFASTA_DDAQuantification.fasta
[18:58] Annotating library proteins with information from the FASTA database
[18:58] Gene names missing for some isoforms
[18:58] Library contains 11951 proteins, and 0 genes
[18:58] Initialising library
[19:01] Saving the library to run_output_Astral/diann_2.3.0/report-lib.parquet.skyline.speclib


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

[19:01] File #1/6
[19:01] Loading run Raw_Astral/mzmls/LFQ_Astral_DIA_15min_50ng_Condition_A_REP1.mzML
[19:12] Pre-processing...
[19:13] 2931 MS1 and 293271 MS2 scans in 977 (inferred) and 977 (encoded) cycles, 105715 precursors in range
[19:13] Calibrating with mass accuracies 21 (MS1), 25 (MS2)
[19:13] RT window set to 0.437994
[19:13] Recommended MS1 mass accuracy setting: 3.2 ppm
[19:14] Searching decoys
[19:14] Main search
[19:15] Removing low confidence identifications
[19:17] Removing interfering precursors
[19:17] Training neural networks on 93588 target and 42832 decoy PSMs
[19:25] Training neural networks on 93557 target and 51527 decoy PSMs
[19:34] IDs at 0.01 FDR: 89676
[19:34] Precursors at 1% peptidoform FDR: 87904
[19:34] Number of IDs at 0.01 FDR: 92395
[19:34] Calculating protein q-values
[19:34] Number of genes identified at 1% FDR: 0 (precursor-level), 0 (protein-level) (inference performed using proteotypic peptides only)
[19:34] Quantification
[19:35] Precursors with scored PTMs at 1% FDR: 3118 out of 3271 considered
[19:35] Precursors with all scored PTM sites unoccupied at 1% FDR: 86272
[19:35] Precursors with PTMs localised (when required) with > 90% confidence: 3038 out of 3118

[19:35] File #2/6
[19:35] Loading run Raw_Astral/mzmls/LFQ_Astral_DIA_15min_50ng_Condition_A_REP2.mzML
[19:47] Pre-processing...
[19:47] 2933 MS1 and 293433 MS2 scans in 978 (inferred) and 978 (encoded) cycles, 105715 precursors in range
[19:47] Calibrating with mass accuracies 21 (MS1), 25 (MS2)
[19:47] RT window set to 0.440846
[19:47] Recommended MS1 mass accuracy setting: 3.1 ppm
[19:48] Searching decoys
[19:48] Main search
[19:49] Removing low confidence identifications
[19:51] Removing interfering precursors
[19:52] Training neural networks on 93671 target and 42968 decoy PSMs
[20:00] Training neural networks on 93636 target and 51527 decoy PSMs
[20:09] IDs at 0.01 FDR: 90081
[20:10] Precursors at 1% peptidoform FDR: 88391
[20:10] Number of IDs at 0.01 FDR: 92799
[20:10] Calculating protein q-values
[20:10] Number of genes identified at 1% FDR: 0 (precursor-level), 0 (protein-level) (inference performed using proteotypic peptides only)
[20:10] Quantification
[20:10] Precursors with scored PTMs at 1% FDR: 3140 out of 3292 considered
[20:10] Precursors with all scored PTM sites unoccupied at 1% FDR: 86738
[20:10] Precursors with PTMs localised (when required) with > 90% confidence: 3070 out of 3140

[20:11] File #3/6
[20:11] Loading run Raw_Astral/mzmls/LFQ_Astral_DIA_15min_50ng_Condition_A_REP3.mzML
[20:22] Pre-processing...
[20:23] 2932 MS1 and 293358 MS2 scans in 978 (inferred) and 978 (encoded) cycles, 105715 precursors in range
[20:23] Calibrating with mass accuracies 21 (MS1), 25 (MS2)
[20:23] RT window set to 0.438787
[20:23] Recommended MS1 mass accuracy setting: 3.1 ppm
[20:24] Searching decoys
[20:24] Main search
[20:24] Removing low confidence identifications
[20:27] Removing interfering precursors
[20:27] Training neural networks on 93633 target and 42796 decoy PSMs
[20:36] Training neural networks on 93597 target and 51730 decoy PSMs
[20:44] IDs at 0.01 FDR: 90420
[20:44] Precursors at 1% peptidoform FDR: 88453
[20:44] Number of IDs at 0.01 FDR: 93094
[20:44] Calculating protein q-values
[20:44] Number of genes identified at 1% FDR: 0 (precursor-level), 0 (protein-level) (inference performed using proteotypic peptides only)
[20:44] Quantification
[20:45] Precursors with scored PTMs at 1% FDR: 3172 out of 3326 considered
[20:45] Precursors with all scored PTM sites unoccupied at 1% FDR: 86733
[20:45] Precursors with PTMs localised (when required) with > 90% confidence: 3095 out of 3172

[20:46] File #4/6
[20:46] Loading run /home/robbe/ProteoBench_diaPASEF/Raw_Astral/mzmls/LFQ_Astral_DIA_15min_50ng_Condition_B_REP1.mzML
[20:56] Pre-processing...
[20:57] 2933 MS1 and 293382 MS2 scans in 978 (inferred) and 978 (encoded) cycles, 105715 precursors in range
[20:57] Calibrating with mass accuracies 21 (MS1), 25 (MS2)
[20:57] RT window set to 0.450608
[20:57] Recommended MS1 mass accuracy setting: 3.3 ppm
[20:57] Searching decoys
[20:57] Main search
[20:58] Removing low confidence identifications
[21:00] Removing interfering precursors
[21:01] Training neural networks on 94026 target and 43420 decoy PSMs
[21:10] Training neural networks on 94002 target and 51921 decoy PSMs
[21:21] IDs at 0.01 FDR: 90629
[21:21] Precursors at 1% peptidoform FDR: 89107
[21:21] Number of IDs at 0.01 FDR: 93142
[21:21] Calculating protein q-values
[21:21] Number of genes identified at 1% FDR: 0 (precursor-level), 0 (protein-level) (inference performed using proteotypic peptides only)
[21:21] Quantification
[21:22] Precursors with scored PTMs at 1% FDR: 3393 out of 3518 considered
[21:22] Precursors with all scored PTM sites unoccupied at 1% FDR: 87210
[21:22] Precursors with PTMs localised (when required) with > 90% confidence: 3321 out of 3393

[21:22] File #5/6
[21:22] Loading run /home/robbe/ProteoBench_diaPASEF/Raw_Astral/mzmls/LFQ_Astral_DIA_15min_50ng_Condition_B_REP2.mzML
[21:33] Pre-processing...
[21:34] 2933 MS1 and 293330 MS2 scans in 978 (inferred) and 978 (encoded) cycles, 105715 precursors in range
[21:34] Calibrating with mass accuracies 21 (MS1), 25 (MS2)
[21:34] RT window set to 0.450786
[21:34] Recommended MS1 mass accuracy setting: 2.9 ppm
[21:35] Searching decoys
[21:35] Main search
[21:36] Removing low confidence identifications
[21:37] Removing interfering precursors
[21:38] Training neural networks on 94031 target and 43244 decoy PSMs
[21:46] Training neural networks on 93995 target and 52035 decoy PSMs
[21:56] IDs at 0.01 FDR: 90986
[21:56] Precursors at 1% peptidoform FDR: 89276
[21:56] Number of IDs at 0.01 FDR: 93379
[21:56] Calculating protein q-values
[21:56] Number of genes identified at 1% FDR: 0 (precursor-level), 0 (protein-level) (inference performed using proteotypic peptides only)
[21:56] Quantification
[21:57] Precursors with scored PTMs at 1% FDR: 3425 out of 3539 considered
[21:57] Precursors with all scored PTM sites unoccupied at 1% FDR: 87221
[21:57] Precursors with PTMs localised (when required) with > 90% confidence: 3351 out of 3425

[21:58] File #6/6
[21:58] Loading run /home/robbe/ProteoBench_diaPASEF/Raw_Astral/mzmls/LFQ_Astral_DIA_15min_50ng_Condition_B_REP3.mzML
[22:08] Pre-processing...
[22:09] 2934 MS1 and 293446 MS2 scans in 978 (inferred) and 978 (encoded) cycles, 105715 precursors in range
[22:09] Calibrating with mass accuracies 21 (MS1), 25 (MS2)
[22:09] RT window set to 0.449523
[22:09] Recommended MS1 mass accuracy setting: 3.1 ppm
[22:09] Searching decoys
[22:09] Main search
[22:10] Removing low confidence identifications
[22:13] Removing interfering precursors
[22:14] Training neural networks on 94095 target and 43086 decoy PSMs
[22:24] Training neural networks on 94054 target and 52029 decoy PSMs
[22:32] IDs at 0.01 FDR: 91314
[22:33] Precursors at 1% peptidoform FDR: 89673
[22:33] Number of IDs at 0.01 FDR: 93536
[22:33] Calculating protein q-values
[22:33] Number of genes identified at 1% FDR: 0 (precursor-level), 0 (protein-level) (inference performed using proteotypic peptides only)
[22:33] Quantification
[22:33] Precursors with scored PTMs at 1% FDR: 3418 out of 3550 considered
[22:33] Precursors with all scored PTM sites unoccupied at 1% FDR: 87591
[22:33] Precursors with PTMs localised (when required) with > 90% confidence: 3340 out of 3418

[22:34] Cross-run analysis
[22:34] Reading quantification information: 6 files
[22:36] Quantifying peptides
[23:26] Quantification parameters: 0.358524, 0.00151028, 0.00163299, 0.0120167, 0.012094, 0.0123072, 0.164831, 0.241959, 0.191054, 0.0134526, 0.0358536, 0.014585, 0.334498, 0.0522175, 0.0725406, 0.0117646
[23:37] Quantifying proteins
[23:37] Calculating q-values for protein and gene groups
[23:38] Calculating global q-values for protein and gene groups
[23:38] Protein groups with global q-value <= 0.01: 10230
[23:40] Compressed report saved to run_output_Astral/diann_2.3.0/report.parquet. Use R 'arrow' or Python 'PyArrow' package to process
[23:40] Stats report saved to run_output_Astral/diann_2.3.0/report.stats.tsv

