
DIA-NN 2.5.0 Academia  (Data-Independent Acquisition by Neural Networks)
Compiled on Apr 12 2026 10:45:33
Current date and time: Mon Apr 27 09:28:04 2026
Logical CPU cores: 128
/home/robbe/bin/diann-2.5.0/diann-linux --f /public/local/ProteoBench/HYE_Astral_Single_Cell/20231123_DIA_240k_20Th_40ms_FAIMSCV-48_gas3p8_200pg_50pg_H_Y_r1.raw --f /public/local/ProteoBench/HYE_Astral_Single_Cell/20231123_DIA_240k_20Th_40ms_FAIMSCV-48_gas3p8_200pg_50pg_H_Y_r2.raw --f /public/local/ProteoBench/HYE_Astral_Single_Cell/20231123_DIA_240k_20Th_40ms_FAIMSCV-48_gas3p8_200pg_50pg_H_Y_r3.raw --f /public/local/ProteoBench/HYE_Astral_Single_Cell/20231123_DIA_240k_20Th_40ms_FAIMSCV-48_gas3p8_240pg_10pg_H_Y_r1.raw --f /public/local/ProteoBench/HYE_Astral_Single_Cell/20231123_DIA_240k_20Th_40ms_FAIMSCV-48_gas3p8_240pg_10pg_H_Y_r2.raw --f /public/local/ProteoBench/HYE_Astral_Single_Cell/20231123_DIA_240k_20Th_40ms_FAIMSCV-48_gas3p8_240pg_10pg_H_Y_r3.raw --fasta /public/local/ProteoBench/fastas/ProteoBenchFASTA_DDAQuantification_noecoli.fasta --out /home/robbe/PB_output/results/test_run/HYE_Astral_Single_Cell/diann_v2.5.0/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_DDAQuantification_noecoli.fasta
[0:05] Processing FASTA
[0:08] Assembling elution groups
[0:15] 7429950 precursors generated
[0:15] Gene names missing for some isoforms
[0:15] Library contains 27293 proteins, and 0 genes
WARNING: no gene information in the FASTA or library: consider using --ids-to-names
[0:20] [0:33] [5:43] [6:21] [6:23] [6:26] Saving the library to /home/robbe/PB_output/results/test_run/HYE_Astral_Single_Cell/diann_v2.5.0/report-lib.predicted.speclib
[6:34] Initialising library
[6:47] Loading spectral library /home/robbe/PB_output/results/test_run/HYE_Astral_Single_Cell/diann_v2.5.0/report-lib.predicted.speclib
[6:50] Library annotated with sequence database(s): /public/local/ProteoBench/fastas/ProteoBenchFASTA_DDAQuantification_noecoli.fasta
[6:51] Spectral library loaded: 27443 protein isoforms, 38575 protein groups and 7429950 precursors in 3511335 elution groups (targets and decoys).
[6:51] Loading protein annotations from FASTA /public/local/ProteoBench/fastas/ProteoBenchFASTA_DDAQuantification_noecoli.fasta
[6:51] Annotating library proteins with information from the FASTA database
[6:51] Gene names missing for some isoforms
[6:51] Library contains 27293 proteins, and 0 genes
WARNING: no gene information in the FASTA or library: consider using --ids-to-names
[6:57] Initialising library
WARNING: it is strongly recommended to enable MBR when analysing with a large library, if this is a quantitative analysis

[7:09] File #1/6
[7:09] Loading run /public/local/ProteoBench/HYE_Astral_Single_Cell/20231123_DIA_240k_20Th_40ms_FAIMSCV-48_gas3p8_200pg_50pg_H_Y_r1.raw
[7:13] Pre-processing...
[7:19] 1842 MS1 and 27799 MS2 scans in 1842 (inferred) and 1842 (encoded) cycles, 5095220 precursors in range
[7:19] Calibrating with mass accuracies 22 (MS1), 24 (MS2)
[8:05] RT window set to 1.48954
[8:05] Peak width: 3.132
[8:05] Scan window radius set to 6
[8:05] Recommended MS1 mass accuracy setting: 2 ppm
[8:40] Searching decoys
[10:06] Main search
[12:56] Removing low confidence identifications
[13:07] Removing interfering precursors
[13:13] Training neural networks on 90461 target and 69217 decoy PSMs
[13:33] Training neural networks on 90461 target and 67827 decoy PSMs
[13:48] Precursors at 1% peptidoform FDR: 34163
[13:49] Number of IDs at 0.01 FDR: 35700
[13:49] Calculating protein q-values
[13:49] Number of genes identified at 1% FDR: 0 (precursor-level), 0 (protein-level) (inference performed using proteotypic peptides only)
[13:49] Quantification
[13:50] Precursors with scored PTMs at 1% FDR: 416 out of 457 considered
[13:50] Precursors with all scored PTM sites unoccupied at 1% FDR: 33896
[13:50] Precursors with PTMs localised (when required) with > 90% confidence: 396 out of 416
[13:50] Quantification information saved to /public/local/ProteoBench/HYE_Astral_Single_Cell/20231123_DIA_240k_20Th_40ms_FAIMSCV-48_gas3p8_200pg_50pg_H_Y_r1.raw.quant

[13:50] File #2/6
[13:50] Loading run /public/local/ProteoBench/HYE_Astral_Single_Cell/20231123_DIA_240k_20Th_40ms_FAIMSCV-48_gas3p8_200pg_50pg_H_Y_r2.raw
[13:54] Pre-processing...
[13:58] 1859 MS1 and 28183 MS2 scans in 1859 (inferred) and 1859 (encoded) cycles, 5095220 precursors in range
[13:59] Calibrating with mass accuracies 22 (MS1), 24 (MS2)
[14:47] RT window set to 1.49216
[14:47] Recommended MS1 mass accuracy setting: 2.1 ppm
[15:23] Searching decoys
[17:00] Main search
[20:11] Removing low confidence identifications
[20:23] Removing interfering precursors
[20:30] Training neural networks on 103085 target and 81845 decoy PSMs
[20:52] Training neural networks on 103085 target and 80492 decoy PSMs
[21:13] Precursors at 1% peptidoform FDR: 36948
[21:14] Number of IDs at 0.01 FDR: 38416
[21:14] Calculating protein q-values
[21:14] Number of genes identified at 1% FDR: 0 (precursor-level), 0 (protein-level) (inference performed using proteotypic peptides only)
[21:14] Quantification
[21:15] Precursors with scored PTMs at 1% FDR: 1619 out of 1739 considered
[21:15] Precursors with all scored PTM sites unoccupied at 1% FDR: 35409
[21:15] Precursors with PTMs localised (when required) with > 90% confidence: 1589 out of 1619
[21:15] Quantification information saved to /public/local/ProteoBench/HYE_Astral_Single_Cell/20231123_DIA_240k_20Th_40ms_FAIMSCV-48_gas3p8_200pg_50pg_H_Y_r2.raw.quant

[21:15] File #3/6
[21:15] Loading run /public/local/ProteoBench/HYE_Astral_Single_Cell/20231123_DIA_240k_20Th_40ms_FAIMSCV-48_gas3p8_200pg_50pg_H_Y_r3.raw
[21:19] Pre-processing...
[21:23] 1856 MS1 and 28105 MS2 scans in 1856 (inferred) and 1856 (encoded) cycles, 5095220 precursors in range
[21:24] Calibrating with mass accuracies 22 (MS1), 24 (MS2)
[22:12] RT window set to 1.5579
[22:12] Recommended MS1 mass accuracy setting: 2 ppm
[22:49] Searching decoys
[24:28] Main search
[27:41] Removing low confidence identifications
[27:52] Removing interfering precursors
[27:59] Training neural networks on 99359 target and 78199 decoy PSMs
[28:23] Training neural networks on 99359 target and 76757 decoy PSMs
[28:41] Precursors at 1% peptidoform FDR: 36024
[28:42] Number of IDs at 0.01 FDR: 37732
[28:42] Calculating protein q-values
[28:42] Number of genes identified at 1% FDR: 0 (precursor-level), 0 (protein-level) (inference performed using proteotypic peptides only)
[28:42] Quantification
[28:42] Precursors with scored PTMs at 1% FDR: 1735 out of 1902 considered
[28:42] Precursors with all scored PTM sites unoccupied at 1% FDR: 34327
[28:42] Precursors with PTMs localised (when required) with > 90% confidence: 1707 out of 1735
[28:43] Quantification information saved to /public/local/ProteoBench/HYE_Astral_Single_Cell/20231123_DIA_240k_20Th_40ms_FAIMSCV-48_gas3p8_200pg_50pg_H_Y_r3.raw.quant

[28:43] File #4/6
[28:43] Loading run /public/local/ProteoBench/HYE_Astral_Single_Cell/20231123_DIA_240k_20Th_40ms_FAIMSCV-48_gas3p8_240pg_10pg_H_Y_r1.raw
[28:46] Pre-processing...
[28:51] 1827 MS1 and 27525 MS2 scans in 1827 (inferred) and 1827 (encoded) cycles, 5095220 precursors in range
[28:51] Calibrating with mass accuracies 22 (MS1), 24 (MS2)
[29:35] RT window set to 1.70983
[29:35] Recommended MS1 mass accuracy setting: 2 ppm
[30:16] Searching decoys
[31:45] Main search
[34:39] Removing low confidence identifications
[34:49] Removing interfering precursors
[34:56] Training neural networks on 85116 target and 65321 decoy PSMs
[35:14] Training neural networks on 85116 target and 63618 decoy PSMs
[35:32] Precursors at 1% peptidoform FDR: 32598
[35:33] Number of IDs at 0.01 FDR: 33897
[35:33] Calculating protein q-values
[35:33] Number of genes identified at 1% FDR: 0 (precursor-level), 0 (protein-level) (inference performed using proteotypic peptides only)
[35:34] Quantification
[35:34] Precursors with scored PTMs at 1% FDR: 282 out of 328 considered
[35:34] Precursors with all scored PTM sites unoccupied at 1% FDR: 32549
[35:34] Precursors with PTMs localised (when required) with > 90% confidence: 263 out of 282
[35:34] Quantification information saved to /public/local/ProteoBench/HYE_Astral_Single_Cell/20231123_DIA_240k_20Th_40ms_FAIMSCV-48_gas3p8_240pg_10pg_H_Y_r1.raw.quant

[35:34] File #5/6
[35:34] Loading run /public/local/ProteoBench/HYE_Astral_Single_Cell/20231123_DIA_240k_20Th_40ms_FAIMSCV-48_gas3p8_240pg_10pg_H_Y_r2.raw
[35:38] Pre-processing...
[35:42] 1831 MS1 and 27624 MS2 scans in 1831 (inferred) and 1831 (encoded) cycles, 5095220 precursors in range
[35:43] Calibrating with mass accuracies 22 (MS1), 24 (MS2)
[36:26] RT window set to 1.49034
[36:27] Recommended MS1 mass accuracy setting: 1.9 ppm
[36:59] Searching decoys
[38:20] Main search
[41:00] Removing low confidence identifications
[41:10] Removing interfering precursors
[41:17] Training neural networks on 92205 target and 72416 decoy PSMs
[41:36] Training neural networks on 92205 target and 71016 decoy PSMs
[41:53] Precursors at 1% peptidoform FDR: 33342
[41:54] Number of IDs at 0.01 FDR: 34944
[41:54] Calculating protein q-values
[41:54] Number of genes identified at 1% FDR: 0 (precursor-level), 0 (protein-level) (inference performed using proteotypic peptides only)
[41:54] Quantification
[41:55] Precursors with scored PTMs at 1% FDR: 484 out of 535 considered
[41:55] Precursors with all scored PTM sites unoccupied at 1% FDR: 32982
[41:55] Precursors with PTMs localised (when required) with > 90% confidence: 456 out of 484
[41:55] Quantification information saved to /public/local/ProteoBench/HYE_Astral_Single_Cell/20231123_DIA_240k_20Th_40ms_FAIMSCV-48_gas3p8_240pg_10pg_H_Y_r2.raw.quant

[41:55] File #6/6
[41:55] Loading run /public/local/ProteoBench/HYE_Astral_Single_Cell/20231123_DIA_240k_20Th_40ms_FAIMSCV-48_gas3p8_240pg_10pg_H_Y_r3.raw
[41:58] Pre-processing...
[42:03] 1831 MS1 and 27589 MS2 scans in 1831 (inferred) and 1831 (encoded) cycles, 5095220 precursors in range
[42:03] Calibrating with mass accuracies 22 (MS1), 24 (MS2)
[42:48] RT window set to 1.62455
[42:48] Recommended MS1 mass accuracy setting: 2 ppm
[43:21] Searching decoys
[44:48] Main search
[47:39] Removing low confidence identifications
[47:49] Removing interfering precursors
[47:55] Training neural networks on 87618 target and 67353 decoy PSMs
[48:14] Training neural networks on 87618 target and 66183 decoy PSMs
[48:31] Precursors at 1% peptidoform FDR: 33455
[48:32] Number of IDs at 0.01 FDR: 34588
[48:32] Calculating protein q-values
[48:32] Number of genes identified at 1% FDR: 0 (precursor-level), 0 (protein-level) (inference performed using proteotypic peptides only)
[48:32] Quantification
[48:33] Precursors with scored PTMs at 1% FDR: 495 out of 537 considered
[48:33] Precursors with all scored PTM sites unoccupied at 1% FDR: 32979
[48:33] Precursors with PTMs localised (when required) with > 90% confidence: 477 out of 495
[48:34] Quantification information saved to /public/local/ProteoBench/HYE_Astral_Single_Cell/20231123_DIA_240k_20Th_40ms_FAIMSCV-48_gas3p8_240pg_10pg_H_Y_r3.raw.quant

[48:34] Cross-run analysis
[48:34] Reading quantification information: 6 files
[48:49] Target precursors at 1% global q-value: 47684
[48:49] Quantifying peptides
[49:18] Quantification parameters: 0.371509, 0.00225989, 0.0147452, 0.0126936, 0.0126395, 0.0126869, 0.311114, 0.16393, 0.190449, 0.0137705, 0.0150661, 0.0145166, 0.354074, 0.118294, 0.1647, 0.0119072
[49:23] Assembling protein groups
[49:24] Quantifying proteins
[49:24] Calculating q-values for protein and gene groups
[49:25] Calculating global q-values for protein and gene groups
[49:25] Protein groups with global q-value <= 0.01: 8259
[49:26] Compressed report saved to /home/robbe/PB_output/results/test_run/HYE_Astral_Single_Cell/diann_v2.5.0/report.parquet. Use R 'arrow' or Python 'PyArrow' package to process
[49:26] Stats report saved to /home/robbe/PB_output/results/test_run/HYE_Astral_Single_Cell/diann_v2.5.0/report.stats.tsv
[49:26] Generating spectral library:
[49:27] 51815 target and 2888 decoy precursors saved
WARNING: 5577 precursors without any fragments annotated were skipped
[49:27] Spectral library saved to /home/robbe/PB_output/results/test_run/HYE_Astral_Single_Cell/diann_v2.5.0/report-lib.parquet

