
DIA-NN 2.2.0 Academia  (Data-Independent Acquisition by Neural Networks)
Compiled on May 29 2025 15:05:00
Current date and time: Wed Apr 22 04:18:44 2026
Logical CPU cores: 128
/home/robbe/bin/diann-2.2.0/diann-linux --f /public/local/ProteoBench/HYE_Astral/LFQ_Astral_DIA_15min_50ng_Condition_A_REP1.raw --f /public/local/ProteoBench/HYE_Astral/LFQ_Astral_DIA_15min_50ng_Condition_A_REP2.raw --f /public/local/ProteoBench/HYE_Astral/LFQ_Astral_DIA_15min_50ng_Condition_A_REP3.raw --f /public/local/ProteoBench/HYE_Astral/LFQ_Astral_DIA_15min_50ng_Condition_B_REP1.raw --f /public/local/ProteoBench/HYE_Astral/LFQ_Astral_DIA_15min_50ng_Condition_B_REP2.raw --f /public/local/ProteoBench/HYE_Astral/LFQ_Astral_DIA_15min_50ng_Condition_B_REP3.raw --fasta /public/local/ProteoBench/fastas/ProteoBenchFASTA_MixedSpecies_HYE.fasta --out /home/robbe/PB_output/results/test_run/HYE_Astral/diann_v2.2.0/report.tsv --threads 32 --missed-cleavages 2 --min-pep-len 7 --max-pep-len 30 --mass-acc 20 --mass-acc-ms1 20 --qvalue 0.01 --protein-qvalue 0.01 --min-pr-charge 2 --max-pr-charge 4 --min-pr-mz 400 --max-pr-mz 1200 --min-fr-mz 200 --max-fr-mz 2000 --unimod4 --var-mod UniMod:35,15.994915,M --gen-spec-lib --fasta-search 

Thread number set to 32
Maximum number of missed cleavages set to 2
Min peptide length set to 7
Max peptide length set to 30
Output will be filtered at 0.01 FDR
Output will be filtered at 0.01 protein-level FDR
Min precursor charge set to 2
Max precursor charge set to 4
Min precursor m/z set to 400
Max precursor m/z set to 1200
Min fragment m/z set to 200
Max fragment m/z set to 2000
Cysteine carbamidomethylation enabled as a fixed modification
Modification UniMod:35 with mass delta 15.9949 at M will be considered as variable
A spectral library will be generated
DIA-NN will carry out FASTA digest for in silico lib generation
Mass accuracy will be fixed to 2e-05 (MS2) and 2e-05 (MS1)
WARNING: FASTA digest mode enabled and raw data are provided, turning on deep learning spectra/RT/IM prediction
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
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 FASTA /public/local/ProteoBench/fastas/ProteoBenchFASTA_MixedSpecies_HYE.fasta
[0:06] Processing FASTA
[0:10] Assembling elution groups
[0:18] 8103720 precursors generated
[0:19] Protein names missing for some isoforms
[0:19] Gene names missing for some isoforms
[0:19] Library contains 31680 proteins, and 0 genes
[0:25] [0:39] [6:12] [6:53] [6:56] [7:01] Saving the library to /home/robbe/PB_output/results/test_run/HYE_Astral/diann_v2.2.0/report-lib.predicted.speclib
[7:07] Initialising library
[7:24] Loading spectral library /home/robbe/PB_output/results/test_run/HYE_Astral/diann_v2.2.0/report-lib.predicted.speclib
[7:28] Library annotated with sequence database(s): /public/local/ProteoBench/fastas/ProteoBenchFASTA_MixedSpecies_HYE.fasta
[7:30] Spectral library loaded: 31832 protein isoforms, 43199 protein groups and 8103720 precursors in 3825450 elution groups.
[7:30] Loading protein annotations from FASTA /public/local/ProteoBench/fastas/ProteoBenchFASTA_MixedSpecies_HYE.fasta
[7:30] Annotating library proteins with information from the FASTA database
[7:30] Protein names missing for some isoforms
[7:30] Gene names missing for some isoforms
[7:30] Library contains 31680 proteins, and 0 genes
[7:36] Initialising library
WARNING: it is strongly recommended to enable MBR when analysing with a large library, if this is a quantitative analysis

[7:50] File #1/6
[7:50] Loading run /public/local/ProteoBench/HYE_Astral/LFQ_Astral_DIA_15min_50ng_Condition_A_REP1.raw
[8:17] Pre-processing...
[8:20] 2931 MS1 and 293271 MS2 scans in 977 (inferred) and 977 (encoded) cycles, 7009928 precursors in range
[8:21] Calibrating with mass accuracies 21 (MS1), 25 (MS2)
[8:41] RT window set to 1.46683
[8:41] Peak width: 2.916
[8:41] Scan window radius set to 6
[8:41] Recommended MS1 mass accuracy setting: 2.8 ppm
[8:51] Searching decoys
[9:52] Main search
[11:48] Removing low confidence identifications
[12:01] Removing interfering precursors
[12:10] Training neural networks on 159430 target and 104132 decoy PSMs
[12:41] Training neural networks on 159430 target and 101594 decoy PSMs
[13:07] Number of IDs at 0.01 FDR: 95301
[13:08] Precursors at 1% peptidoform FDR: 93310
[13:09] Calculating protein q-values
[13:10] Number of genes identified at 1% FDR: 0 (precursor-level), 0 (protein-level) (inference performed using proteotypic peptides only)
[13:10] Quantification
[13:11] Precursors with scored PTMs at 1% FDR: 2026 out of 2211 considered
[13:11] Precursors with all scored PTM sites unoccupied at 1% FDR: 91284
[13:11] Precursors with PTMs localised (when required) with > 90% confidence: 1946 out of 2026
[13:12] Quantification information saved to /public/local/ProteoBench/HYE_Astral/LFQ_Astral_DIA_15min_50ng_Condition_A_REP1.raw.quant

[13:12] File #2/6
[13:12] Loading run /public/local/ProteoBench/HYE_Astral/LFQ_Astral_DIA_15min_50ng_Condition_A_REP2.raw
[13:38] Pre-processing...
[13:41] 2933 MS1 and 293433 MS2 scans in 978 (inferred) and 978 (encoded) cycles, 7009928 precursors in range
[13:42] Calibrating with mass accuracies 21 (MS1), 25 (MS2)
[13:59] RT window set to 1.28513
[13:59] Recommended MS1 mass accuracy setting: 2.7 ppm
[14:09] Searching decoys
[15:03] Main search
[16:46] Removing low confidence identifications
[16:58] Removing interfering precursors
[17:05] Training neural networks on 159711 target and 106939 decoy PSMs
[17:37] Training neural networks on 159711 target and 103801 decoy PSMs
[18:04] Number of IDs at 0.01 FDR: 96264
[18:05] Precursors at 1% peptidoform FDR: 94073
[18:06] Calculating protein q-values
[18:06] Number of genes identified at 1% FDR: 0 (precursor-level), 0 (protein-level) (inference performed using proteotypic peptides only)
[18:06] Quantification
[18:07] Precursors with scored PTMs at 1% FDR: 2181 out of 2326 considered
[18:07] Precursors with all scored PTM sites unoccupied at 1% FDR: 91892
[18:07] Precursors with PTMs localised (when required) with > 90% confidence: 2111 out of 2181
[18:09] Quantification information saved to /public/local/ProteoBench/HYE_Astral/LFQ_Astral_DIA_15min_50ng_Condition_A_REP2.raw.quant

[18:09] File #3/6
[18:09] Loading run /public/local/ProteoBench/HYE_Astral/LFQ_Astral_DIA_15min_50ng_Condition_A_REP3.raw
[18:32] Pre-processing...
[18:34] 2932 MS1 and 293358 MS2 scans in 978 (inferred) and 978 (encoded) cycles, 7009928 precursors in range
[18:35] Calibrating with mass accuracies 21 (MS1), 25 (MS2)
[18:53] RT window set to 1.22158
[18:53] Recommended MS1 mass accuracy setting: 2.5 ppm
[19:01] Searching decoys
[19:54] Main search
[21:32] Removing low confidence identifications
[21:44] Removing interfering precursors
[21:52] Training neural networks on 161860 target and 107878 decoy PSMs
[22:24] Training neural networks on 161860 target and 105134 decoy PSMs
[22:52] Number of IDs at 0.01 FDR: 96384
[22:53] Precursors at 1% peptidoform FDR: 94217
[22:54] Calculating protein q-values
[22:54] Number of genes identified at 1% FDR: 0 (precursor-level), 0 (protein-level) (inference performed using proteotypic peptides only)
[22:54] Quantification
[22:55] Precursors with scored PTMs at 1% FDR: 2173 out of 2358 considered
[22:55] Precursors with all scored PTM sites unoccupied at 1% FDR: 92044
[22:55] Precursors with PTMs localised (when required) with > 90% confidence: 2091 out of 2173
[22:56] Quantification information saved to /public/local/ProteoBench/HYE_Astral/LFQ_Astral_DIA_15min_50ng_Condition_A_REP3.raw.quant

[22:56] File #4/6
[22:56] Loading run /public/local/ProteoBench/HYE_Astral/LFQ_Astral_DIA_15min_50ng_Condition_B_REP1.raw
[23:17] Pre-processing...
[23:20] 2933 MS1 and 293382 MS2 scans in 978 (inferred) and 978 (encoded) cycles, 7009928 precursors in range
[23:20] Calibrating with mass accuracies 21 (MS1), 25 (MS2)
[23:38] RT window set to 1.29961
[23:38] Recommended MS1 mass accuracy setting: 3.1 ppm
[23:48] Searching decoys
[24:45] Main search
[26:28] Removing low confidence identifications
[26:40] Removing interfering precursors
[26:48] Training neural networks on 165936 target and 113377 decoy PSMs
[27:26] Training neural networks on 165936 target and 109861 decoy PSMs
[27:55] Number of IDs at 0.01 FDR: 96767
[27:55] Precursors at 1% peptidoform FDR: 94443
[27:56] Calculating protein q-values
[27:57] Number of genes identified at 1% FDR: 0 (precursor-level), 0 (protein-level) (inference performed using proteotypic peptides only)
[27:57] Quantification
[27:58] Precursors with scored PTMs at 1% FDR: 2676 out of 2907 considered
[27:58] Precursors with all scored PTM sites unoccupied at 1% FDR: 91767
[27:58] Precursors with PTMs localised (when required) with > 90% confidence: 2568 out of 2676
[27:59] Quantification information saved to /public/local/ProteoBench/HYE_Astral/LFQ_Astral_DIA_15min_50ng_Condition_B_REP1.raw.quant

[27:59] File #5/6
[27:59] Loading run /public/local/ProteoBench/HYE_Astral/LFQ_Astral_DIA_15min_50ng_Condition_B_REP2.raw
[28:15] Pre-processing...
[28:17] 2933 MS1 and 293330 MS2 scans in 978 (inferred) and 978 (encoded) cycles, 7009928 precursors in range
[28:18] Calibrating with mass accuracies 21 (MS1), 25 (MS2)
[28:35] RT window set to 1.34123
[28:36] Recommended MS1 mass accuracy setting: 2.7 ppm
[28:45] Searching decoys
[29:43] Main search
[31:32] Removing low confidence identifications
[31:44] Removing interfering precursors
[31:52] Training neural networks on 159983 target and 106667 decoy PSMs
[32:23] Training neural networks on 159983 target and 103487 decoy PSMs
[32:48] Number of IDs at 0.01 FDR: 96419
[32:49] Precursors at 1% peptidoform FDR: 94407
[32:50] Calculating protein q-values
[32:51] Number of genes identified at 1% FDR: 0 (precursor-level), 0 (protein-level) (inference performed using proteotypic peptides only)
[32:51] Quantification
[32:51] Precursors with scored PTMs at 1% FDR: 2689 out of 2903 considered
[32:51] Precursors with all scored PTM sites unoccupied at 1% FDR: 91718
[32:51] Precursors with PTMs localised (when required) with > 90% confidence: 2597 out of 2689
[32:53] Quantification information saved to /public/local/ProteoBench/HYE_Astral/LFQ_Astral_DIA_15min_50ng_Condition_B_REP2.raw.quant

[32:53] File #6/6
[32:53] Loading run /public/local/ProteoBench/HYE_Astral/LFQ_Astral_DIA_15min_50ng_Condition_B_REP3.raw
[33:09] Pre-processing...
[33:12] 2934 MS1 and 293446 MS2 scans in 978 (inferred) and 978 (encoded) cycles, 7009928 precursors in range
[33:12] Calibrating with mass accuracies 21 (MS1), 25 (MS2)
[33:29] RT window set to 1.18455
[33:30] Recommended MS1 mass accuracy setting: 2.8 ppm
[33:38] Searching decoys
[34:30] Main search
[36:06] Removing low confidence identifications
[36:20] Removing interfering precursors
[36:29] Training neural networks on 162438 target and 109877 decoy PSMs
[37:00] Training neural networks on 162438 target and 106407 decoy PSMs
[37:26] Number of IDs at 0.01 FDR: 97193
[37:26] Precursors at 1% peptidoform FDR: 94523
[37:27] Calculating protein q-values
[37:28] Number of genes identified at 1% FDR: 0 (precursor-level), 0 (protein-level) (inference performed using proteotypic peptides only)
[37:28] Quantification
[37:29] Precursors with scored PTMs at 1% FDR: 2675 out of 2974 considered
[37:29] Precursors with all scored PTM sites unoccupied at 1% FDR: 91848
[37:29] Precursors with PTMs localised (when required) with > 90% confidence: 2567 out of 2675
[37:30] Quantification information saved to /public/local/ProteoBench/HYE_Astral/LFQ_Astral_DIA_15min_50ng_Condition_B_REP3.raw.quant

[37:30] Cross-run analysis
[37:30] Reading quantification information: 6 files
[37:49] Quantifying peptides
[39:46] Quantification parameters: 0.366677, 0.00142049, 0.00144087, 0.0340085, 0.0444277, 0.0647273, 0.309082, 0.0966272, 0.137917, 0.114532, 0.0508378, 0.0586652, 0.215054, 0.0510727, 0.0587186, 0.0115706
[40:56] Assembling protein groups
[40:59] Quantifying proteins
[40:59] Calculating q-values for protein and gene groups
[41:00] Calculating global q-values for protein and gene groups
[41:01] Protein groups with global q-value <= 0.01: 11220
[41:03] Compressed report saved to /home/robbe/PB_output/results/test_run/HYE_Astral/diann_v2.2.0/report.parquet. Use R 'arrow' or Python 'PyArrow' package to process
[41:03] Stats report saved to /home/robbe/PB_output/results/test_run/HYE_Astral/diann_v2.2.0/report.stats.tsv
[41:04] Generating spectral library:
[41:05] 123552 target and 1179 decoy precursors saved
WARNING: 1744 precursors without any fragments annotated were skipped
[41:05] Spectral library saved to /home/robbe/PB_output/results/test_run/HYE_Astral/diann_v2.2.0/report-lib.parquet

