
DIA-NN 2.1.0 Academia  (Data-Independent Acquisition by Neural Networks)
Compiled on Mar 23 2025 15:49:03
Current date and time: Wed Apr 22 08:01:15 2026
Logical CPU cores: 128
/home/robbe/bin/diann-2.1.0/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_v2.1.0/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
[0:09] Assembling elution groups
[0:17] 8103720 precursors generated
[0:17] Protein names missing for some isoforms
[0:17] Gene names missing for some isoforms
[0:17] Library contains 31680 proteins, and 0 genes
[0:24] [0:39] [6:26] [7:09] [7:11] [7:15] Saving the library to /home/robbe/PB_output/results/test_run/HYE_Astral/diann_v2.1.0/report-lib.predicted.speclib
[7:20] Initialising library
[7:45] Loading spectral library /home/robbe/PB_output/results/test_run/HYE_Astral/diann_v2.1.0/report-lib.predicted.speclib
[7:49] Library annotated with sequence database(s): /public/local/ProteoBench/fastas/ProteoBenchFASTA_MixedSpecies_HYE.fasta
[7:50] Spectral library loaded: 31832 protein isoforms, 43199 protein groups and 8103720 precursors in 3825450 elution groups.
[7:50] Loading protein annotations from FASTA /public/local/ProteoBench/fastas/ProteoBenchFASTA_MixedSpecies_HYE.fasta
[7:51] Annotating library proteins with information from the FASTA database
[7:51] Protein names missing for some isoforms
[7:51] Gene names missing for some isoforms
[7:51] Library contains 31680 proteins, and 0 genes
[7:57] Initialising library
WARNING: it is strongly recommended to enable MBR when analysing with a large library, if this is a quantitative analysis

[8:23] File #1/6
[8:23] Loading run /public/local/ProteoBench/HYE_Astral/LFQ_Astral_DIA_15min_50ng_Condition_A_REP1.raw
[9:00] Pre-processing...
[9:05] 2931 MS1 and 293271 MS2 scans in 977 (inferred) and 977 (encoded) cycles, 7009928 precursors in range
[9:07] Calibrating with mass accuracies 21 (MS1), 25 (MS2)
[9:36] RT window set to 1.24129
[9:36] Peak width: 2.74
[9:36] Scan window radius set to 6
[9:36] Recommended MS1 mass accuracy setting: 2.3 ppm
[10:06] Searching decoys
[10:52] Main search
[12:20] Removing low confidence identifications
[12:33] Removing interfering precursors
[12:43] Training neural networks on 153565 target and 98028 decoy PSMs
[13:16] Training neural networks on 153565 target and 95540 decoy PSMs
[13:43] Number of IDs at 0.01 FDR: 92158
[13:43] Precursors at 1% peptidoform FDR: 89924
[13:44] Calculating protein q-values
[13:45] Number of genes identified at 1% FDR: 0 (precursor-level), 0 (protein-level) (inference performed using proteotypic peptides only)
[13:45] Quantification
[13:46] Precursors with scored PTMs at 1% FDR: 2039 out of 2234 considered
[13:46] Precursors with all scored PTM sites unoccupied at 1% FDR: 87885
[13:46] Precursors with PTMs localised (when required) with > 90% confidence: 1974 out of 2039
[13:47] Quantification information saved to /public/local/ProteoBench/HYE_Astral/LFQ_Astral_DIA_15min_50ng_Condition_A_REP1.raw.quant

[13:47] File #2/6
[13:47] Loading run /public/local/ProteoBench/HYE_Astral/LFQ_Astral_DIA_15min_50ng_Condition_A_REP2.raw
[14:25] Pre-processing...
[14:30] 2933 MS1 and 293433 MS2 scans in 978 (inferred) and 978 (encoded) cycles, 7009928 precursors in range
[14:32] Calibrating with mass accuracies 21 (MS1), 25 (MS2)
[15:01] RT window set to 1.21512
[15:01] Recommended MS1 mass accuracy setting: 2.5 ppm
[15:29] Searching decoys
[16:12] Main search
[17:37] Removing low confidence identifications
[17:49] Removing interfering precursors
[17:58] Training neural networks on 158016 target and 104264 decoy PSMs
[18:33] Training neural networks on 158016 target and 101971 decoy PSMs
[18:59] Number of IDs at 0.01 FDR: 93216
[19:00] Precursors at 1% peptidoform FDR: 91089
[19:01] Calculating protein q-values
[19:02] Number of genes identified at 1% FDR: 0 (precursor-level), 0 (protein-level) (inference performed using proteotypic peptides only)
[19:02] Quantification
[19:03] Precursors with scored PTMs at 1% FDR: 2151 out of 2313 considered
[19:03] Precursors with all scored PTM sites unoccupied at 1% FDR: 88938
[19:03] Precursors with PTMs localised (when required) with > 90% confidence: 2091 out of 2151
[19:04] Quantification information saved to /public/local/ProteoBench/HYE_Astral/LFQ_Astral_DIA_15min_50ng_Condition_A_REP2.raw.quant

[19:04] File #3/6
[19:04] Loading run /public/local/ProteoBench/HYE_Astral/LFQ_Astral_DIA_15min_50ng_Condition_A_REP3.raw
[19:43] Pre-processing...
[19:48] 2932 MS1 and 293358 MS2 scans in 978 (inferred) and 978 (encoded) cycles, 7009928 precursors in range
[19:49] Calibrating with mass accuracies 21 (MS1), 25 (MS2)
[20:19] RT window set to 1.38026
[20:19] Recommended MS1 mass accuracy setting: 2.4 ppm
[20:47] Searching decoys
[21:34] Main search
[23:08] Removing low confidence identifications
[23:21] Removing interfering precursors
[23:30] Training neural networks on 157757 target and 103946 decoy PSMs
[24:04] Training neural networks on 157757 target and 101160 decoy PSMs
[24:30] Number of IDs at 0.01 FDR: 93642
[24:31] Precursors at 1% peptidoform FDR: 91634
[24:32] Calculating protein q-values
[24:32] Number of genes identified at 1% FDR: 0 (precursor-level), 0 (protein-level) (inference performed using proteotypic peptides only)
[24:32] Quantification
[24:33] Precursors with scored PTMs at 1% FDR: 2140 out of 2311 considered
[24:33] Precursors with all scored PTM sites unoccupied at 1% FDR: 89494
[24:33] Precursors with PTMs localised (when required) with > 90% confidence: 2065 out of 2140
[24:35] Quantification information saved to /public/local/ProteoBench/HYE_Astral/LFQ_Astral_DIA_15min_50ng_Condition_A_REP3.raw.quant

[24:35] File #4/6
[24:35] Loading run /public/local/ProteoBench/HYE_Astral/LFQ_Astral_DIA_15min_50ng_Condition_B_REP1.raw
[25:10] Pre-processing...
[25:16] 2933 MS1 and 293382 MS2 scans in 978 (inferred) and 978 (encoded) cycles, 7009928 precursors in range
[25:17] Calibrating with mass accuracies 21 (MS1), 25 (MS2)
[25:46] RT window set to 1.37058
[25:46] Recommended MS1 mass accuracy setting: 2.6 ppm
[26:19] Searching decoys
[27:10] Main search
[28:49] Removing low confidence identifications
[29:01] Removing interfering precursors
[29:11] Training neural networks on 164493 target and 110733 decoy PSMs
[29:48] Training neural networks on 164493 target and 107447 decoy PSMs
[30:18] Number of IDs at 0.01 FDR: 94245
[30:19] Precursors at 1% peptidoform FDR: 92306
[30:20] Calculating protein q-values
[30:20] Number of genes identified at 1% FDR: 0 (precursor-level), 0 (protein-level) (inference performed using proteotypic peptides only)
[30:21] Quantification
[30:21] Precursors with scored PTMs at 1% FDR: 2692 out of 2871 considered
[30:21] Precursors with all scored PTM sites unoccupied at 1% FDR: 89614
[30:21] Precursors with PTMs localised (when required) with > 90% confidence: 2608 out of 2692
[30:23] Quantification information saved to /public/local/ProteoBench/HYE_Astral/LFQ_Astral_DIA_15min_50ng_Condition_B_REP1.raw.quant

[30:23] File #5/6
[30:23] Loading run /public/local/ProteoBench/HYE_Astral/LFQ_Astral_DIA_15min_50ng_Condition_B_REP2.raw
[30:59] Pre-processing...
[31:04] 2933 MS1 and 293330 MS2 scans in 978 (inferred) and 978 (encoded) cycles, 7009928 precursors in range
[31:05] Calibrating with mass accuracies 21 (MS1), 25 (MS2)
[31:36] RT window set to 1.32019
[31:36] Recommended MS1 mass accuracy setting: 2.2 ppm
[32:04] Searching decoys
[32:53] Main search
[34:25] Removing low confidence identifications
[34:38] Removing interfering precursors
[34:48] Training neural networks on 155669 target and 102397 decoy PSMs
[35:20] Training neural networks on 155669 target and 100110 decoy PSMs
[35:50] Number of IDs at 0.01 FDR: 93184
[35:50] Precursors at 1% peptidoform FDR: 91316
[35:51] Calculating protein q-values
[35:52] Number of genes identified at 1% FDR: 0 (precursor-level), 0 (protein-level) (inference performed using proteotypic peptides only)
[35:52] Quantification
[35:53] Precursors with scored PTMs at 1% FDR: 2647 out of 2856 considered
[35:53] Precursors with all scored PTM sites unoccupied at 1% FDR: 88669
[35:53] Precursors with PTMs localised (when required) with > 90% confidence: 2578 out of 2647
[35:54] Quantification information saved to /public/local/ProteoBench/HYE_Astral/LFQ_Astral_DIA_15min_50ng_Condition_B_REP2.raw.quant

[35:54] File #6/6
[35:54] Loading run /public/local/ProteoBench/HYE_Astral/LFQ_Astral_DIA_15min_50ng_Condition_B_REP3.raw
[36:27] Pre-processing...
[36:32] 2934 MS1 and 293446 MS2 scans in 978 (inferred) and 978 (encoded) cycles, 7009928 precursors in range
[36:33] Calibrating with mass accuracies 21 (MS1), 25 (MS2)
[37:03] RT window set to 1.20474
[37:03] Recommended MS1 mass accuracy setting: 2.5 ppm
[37:34] Searching decoys
[38:18] Main search
[39:42] Removing low confidence identifications
[39:55] Removing interfering precursors
[40:05] Training neural networks on 164278 target and 110606 decoy PSMs
[40:42] Training neural networks on 164278 target and 107779 decoy PSMs
[41:12] Number of IDs at 0.01 FDR: 94659
[41:12] Precursors at 1% peptidoform FDR: 92146
[41:14] Calculating protein q-values
[41:14] Number of genes identified at 1% FDR: 0 (precursor-level), 0 (protein-level) (inference performed using proteotypic peptides only)
[41:14] Quantification
[41:15] Precursors with scored PTMs at 1% FDR: 2707 out of 2893 considered
[41:15] Precursors with all scored PTM sites unoccupied at 1% FDR: 89439
[41:15] Precursors with PTMs localised (when required) with > 90% confidence: 2617 out of 2707
[41:16] Quantification information saved to /public/local/ProteoBench/HYE_Astral/LFQ_Astral_DIA_15min_50ng_Condition_B_REP3.raw.quant

[41:16] Cross-run analysis
[41:16] Reading quantification information: 6 files
[41:35] Quantifying peptides
[43:26] Quantification parameters: 0.356965, 0.00143351, 0.00143153, 0.0262829, 0.0479713, 0.0635208, 0.299997, 0.0919429, 0.134869, 0.104526, 0.0505519, 0.0569417, 0.215155, 0.0506971, 0.0573049, 0.0122196
[44:33] Assembling protein groups
[44:36] Quantifying proteins
[44:36] Calculating q-values for protein and gene groups
[44:37] Calculating global q-values for protein and gene groups
[44:37] Protein groups with global q-value <= 0.01: 11092
[44:40] Compressed report saved to /home/robbe/PB_output/results/test_run/HYE_Astral/diann_v2.1.0/report.parquet. Use R 'arrow' or Python 'PyArrow' package to process
[44:40] Stats report saved to /home/robbe/PB_output/results/test_run/HYE_Astral/diann_v2.1.0/report.stats.tsv
[44:41] Generating spectral library:
[44:42] 121766 target and 1176 decoy precursors saved
WARNING: 1186 precursors without any fragments annotated were skipped
[44:42] Spectral library saved to /home/robbe/PB_output/results/test_run/HYE_Astral/diann_v2.1.0/report-lib.parquet

