DIA-NN 1.9.2 (Data-Independent Acquisition by Neural Networks)
Compiled on Oct 31 2024 04:27:44
Current date and time: Wed Apr 22 13:12:05 2026
Logical CPU cores: 128
/home/robbe/bin/diann-1.9.2/diann-linux --f /public/local/ProteoBench/PYE_diaPASEF/A9_G_DIA_nLC_tTOF_R1.d --f /public/local/ProteoBench/PYE_diaPASEF/A9_G_DIA_nLC_tTOF_R2.d --f /public/local/ProteoBench/PYE_diaPASEF/A9_G_DIA_nLC_tTOF_R3.d --f /public/local/ProteoBench/PYE_diaPASEF/A9_G_DIA_nLC_tTOF_R4.d --f /public/local/ProteoBench/PYE_diaPASEF/A9_G_DIA_nLC_tTOF_R5.d --f /public/local/ProteoBench/PYE_diaPASEF/A9_G_DIA_nLC_tTOF_R6.d --f /public/local/ProteoBench/PYE_diaPASEF/B9_G_DIA_nLC_tTOF_R1.d --f /public/local/ProteoBench/PYE_diaPASEF/B9_G_DIA_nLC_tTOF_R2.d --f /public/local/ProteoBench/PYE_diaPASEF/B9_G_DIA_nLC_tTOF_R3.d --f /public/local/ProteoBench/PYE_diaPASEF/B9_G_DIA_nLC_tTOF_R4.d --f /public/local/ProteoBench/PYE_diaPASEF/B9_G_DIA_nLC_tTOF_R5.d --f /public/local/ProteoBench/PYE_diaPASEF/B9_G_DIA_nLC_tTOF_R6.d --fasta /public/local/ProteoBench/fastas/ProteoBenchFASTA_MixedSpecies_HYE.fasta --out /home/robbe/PB_output/results/test_run/PYE_diaPASEF/diann_v1.9.2/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: it is strongly recommended to first generate an in silico-predicted library in a separate pipeline step and then use it 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 scored: UniMod:35 

12 files will be processed
[0:00] Loading FASTA /public/local/ProteoBench/fastas/ProteoBenchFASTA_MixedSpecies_HYE.fasta
[0:06] Processing FASTA
[0:14] Assembling elution groups
[0:22] 8103720 precursors generated
[0:22] Protein names missing for some isoforms
[0:22] Gene names missing for some isoforms
[0:22] Library contains 31680 proteins, and 0 genes
[0:28] [0:43] [6:29] [7:11] [7:14] [7:18] Saving the library to /home/robbe/PB_output/results/test_run/PYE_diaPASEF/diann_v1.9.2/report-lib.predicted.speclib
[7:28] Initialising library
[7:45] Loading spectral library /home/robbe/PB_output/results/test_run/PYE_diaPASEF/diann_v1.9.2/report-lib.predicted.speclib
[7:49] Library annotated with sequence database(s): /public/local/ProteoBench/fastas/ProteoBenchFASTA_MixedSpecies_HYE.fasta
[7:51] Spectral library loaded: 31832 protein isoforms, 43199 protein groups and 8103720 precursors in 3825450 elution groups.
[7:51] 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

[8:19] File #1/12
[8:19] Loading run /public/local/ProteoBench/PYE_diaPASEF/A9_G_DIA_nLC_tTOF_R1.d
WARNING: for most Slice/DIA-PASEF datasets it is better to manually fix both the MS1 and MS2 mass accuracies to values in the range 10-15 ppm
[8:27] 7931928 library precursors are potentially detectable
[8:28] Calibrating with mass accuracies 20 (MS1), 20 (MS2)
[11:17] RT window set to 1.92756
[11:17] Ion mobility window set to 0.0386483
[11:17] Peak width: 3.208
[11:17] Scan window radius set to 7
[11:18] Recommended MS1 mass accuracy setting: 12.2031 ppm
[16:19] Removing low confidence identifications
[18:12] Precursors at 1% peptidoform FDR: 6755
[18:12] Removing interfering precursors
[18:16] Training neural networks on 35196 PSMs
[18:19] Number of IDs at 0.01 FDR: 11838
[18:19] Precursors at 1% peptidoform FDR: 10249
[18:20] Calculating protein q-values
[18:21] Number of genes identified at 1% FDR: 0 (precursor-level), 0 (protein-level) (inference performed using proteotypic peptides only)
[18:21] Quantification
[18:21] Precursors with monitored PTMs at 1% FDR: 81 out of 2182 considered
[18:21] Unmodified precursors with monitored PTM sites at 1% FDR: 1836
[18:21] Precursors with PTMs localised (when required) with > 90% confidence: 79 out of 81
[18:21] Quantification information saved to /public/local/ProteoBench/PYE_diaPASEF/A9_G_DIA_nLC_tTOF_R1.d.quant

[18:21] File #2/12
[18:21] Loading run /public/local/ProteoBench/PYE_diaPASEF/A9_G_DIA_nLC_tTOF_R2.d
[18:29] 7931928 library precursors are potentially detectable
[18:30] Calibrating with mass accuracies 20 (MS1), 20 (MS2)
[21:20] RT window set to 2.12635
[21:20] Ion mobility window set to 0.0377218
[21:20] Recommended MS1 mass accuracy setting: 13.4084 ppm
[26:48] Removing low confidence identifications
[28:52] Precursors at 1% peptidoform FDR: 7988
[28:52] Removing interfering precursors
[28:57] Training neural networks on 40266 PSMs
[28:59] Number of IDs at 0.01 FDR: 13160
[29:00] Precursors at 1% peptidoform FDR: 11156
[29:01] Calculating protein q-values
[29:01] Number of genes identified at 1% FDR: 0 (precursor-level), 0 (protein-level) (inference performed using proteotypic peptides only)
[29:01] Quantification
[29:01] Precursors with monitored PTMs at 1% FDR: 23 out of 2495 considered
[29:01] Unmodified precursors with monitored PTM sites at 1% FDR: 2060
[29:01] Precursors with PTMs localised (when required) with > 90% confidence: 21 out of 23
[29:02] Quantification information saved to /public/local/ProteoBench/PYE_diaPASEF/A9_G_DIA_nLC_tTOF_R2.d.quant

[29:02] File #3/12
[29:02] Loading run /public/local/ProteoBench/PYE_diaPASEF/A9_G_DIA_nLC_tTOF_R3.d
[29:11] 7931928 library precursors are potentially detectable
[29:12] Calibrating with mass accuracies 20 (MS1), 20 (MS2)
[32:03] RT window set to 1.924
[32:03] Ion mobility window set to 0.040186
[32:03] Recommended MS1 mass accuracy setting: 13.224 ppm
[37:02] Removing low confidence identifications
[38:55] Precursors at 1% peptidoform FDR: 7686
[38:55] Removing interfering precursors
[39:00] Training neural networks on 38169 PSMs
[39:02] Number of IDs at 0.01 FDR: 12186
[39:02] Precursors at 1% peptidoform FDR: 10719
[39:03] Calculating protein q-values
[39:04] Number of genes identified at 1% FDR: 0 (precursor-level), 0 (protein-level) (inference performed using proteotypic peptides only)
[39:04] Quantification
[39:04] Precursors with monitored PTMs at 1% FDR: 102 out of 2303 considered
[39:04] Unmodified precursors with monitored PTM sites at 1% FDR: 1920
[39:04] Precursors with PTMs localised (when required) with > 90% confidence: 99 out of 102
[39:05] Quantification information saved to /public/local/ProteoBench/PYE_diaPASEF/A9_G_DIA_nLC_tTOF_R3.d.quant

[39:05] File #4/12
[39:05] Loading run /public/local/ProteoBench/PYE_diaPASEF/A9_G_DIA_nLC_tTOF_R4.d
[39:14] 7931928 library precursors are potentially detectable
[39:15] Calibrating with mass accuracies 20 (MS1), 20 (MS2)
[42:11] RT window set to 2.07605
[42:11] Ion mobility window set to 0.0398881
[42:11] Recommended MS1 mass accuracy setting: 12.2096 ppm
[47:46] Removing low confidence identifications
[49:53] Precursors at 1% peptidoform FDR: 7870
[49:53] Removing interfering precursors
[49:58] Training neural networks on 40994 PSMs
[50:00] Number of IDs at 0.01 FDR: 13354
[50:01] Precursors at 1% peptidoform FDR: 11103
[50:01] Calculating protein q-values
[50:02] Number of genes identified at 1% FDR: 0 (precursor-level), 0 (protein-level) (inference performed using proteotypic peptides only)
[50:02] Quantification
[50:02] Precursors with monitored PTMs at 1% FDR: 88 out of 2546 considered
[50:02] Unmodified precursors with monitored PTM sites at 1% FDR: 1997
[50:02] Precursors with PTMs localised (when required) with > 90% confidence: 82 out of 88
[50:03] Quantification information saved to /public/local/ProteoBench/PYE_diaPASEF/A9_G_DIA_nLC_tTOF_R4.d.quant

[50:03] File #5/12
[50:03] Loading run /public/local/ProteoBench/PYE_diaPASEF/A9_G_DIA_nLC_tTOF_R5.d
[50:12] 7931928 library precursors are potentially detectable
[50:13] Calibrating with mass accuracies 20 (MS1), 20 (MS2)
[53:09] RT window set to 1.94948
[53:09] Ion mobility window set to 0.0421572
[53:09] Recommended MS1 mass accuracy setting: 12.2728 ppm
[58:34] Removing low confidence identifications
[60:36] Precursors at 1% peptidoform FDR: 8286
[60:36] Removing interfering precursors
[60:40] Training neural networks on 38896 PSMs
[60:42] Number of IDs at 0.01 FDR: 13249
[60:43] Precursors at 1% peptidoform FDR: 11083
[60:44] Calculating protein q-values
[60:44] Number of genes identified at 1% FDR: 0 (precursor-level), 0 (protein-level) (inference performed using proteotypic peptides only)
[60:44] Quantification
[60:45] Precursors with monitored PTMs at 1% FDR: 100 out of 2464 considered
[60:45] Unmodified precursors with monitored PTM sites at 1% FDR: 2019
[60:45] Precursors with PTMs localised (when required) with > 90% confidence: 97 out of 100
[60:45] Quantification information saved to /public/local/ProteoBench/PYE_diaPASEF/A9_G_DIA_nLC_tTOF_R5.d.quant

[60:45] File #6/12
[60:45] Loading run /public/local/ProteoBench/PYE_diaPASEF/A9_G_DIA_nLC_tTOF_R6.d
[60:54] 7931928 library precursors are potentially detectable
[60:56] Calibrating with mass accuracies 20 (MS1), 20 (MS2)
[63:47] RT window set to 1.86747
[63:47] Ion mobility window set to 0.0397635
[63:47] Recommended MS1 mass accuracy setting: 12.0173 ppm
[68:49] Removing low confidence identifications
[70:43] Precursors at 1% peptidoform FDR: 8065
[70:44] Removing interfering precursors
[70:48] Training neural networks on 39720 PSMs
[70:50] Number of IDs at 0.01 FDR: 12992
[70:51] Precursors at 1% peptidoform FDR: 10680
[70:52] Calculating protein q-values
[70:52] Number of genes identified at 1% FDR: 0 (precursor-level), 0 (protein-level) (inference performed using proteotypic peptides only)
[70:52] Quantification
[70:52] Precursors with monitored PTMs at 1% FDR: 54 out of 2421 considered
[70:52] Unmodified precursors with monitored PTM sites at 1% FDR: 1877
[70:52] Precursors with PTMs localised (when required) with > 90% confidence: 47 out of 54
[70:53] Quantification information saved to /public/local/ProteoBench/PYE_diaPASEF/A9_G_DIA_nLC_tTOF_R6.d.quant

[70:53] File #7/12
[70:53] Loading run /public/local/ProteoBench/PYE_diaPASEF/B9_G_DIA_nLC_tTOF_R1.d
[71:02] 7931928 library precursors are potentially detectable
[71:03] Calibrating with mass accuracies 20 (MS1), 20 (MS2)
[73:54] RT window set to 2.12226
[73:54] Ion mobility window set to 0.0382781
[73:54] Recommended MS1 mass accuracy setting: 13.2719 ppm
[79:13] Removing low confidence identifications
[81:12] Precursors at 1% peptidoform FDR: 7459
[81:12] Removing interfering precursors
[81:17] Training neural networks on 38497 PSMs
[81:19] Number of IDs at 0.01 FDR: 12834
[81:20] Precursors at 1% peptidoform FDR: 10983
[81:20] Calculating protein q-values
[81:21] Number of genes identified at 1% FDR: 0 (precursor-level), 0 (protein-level) (inference performed using proteotypic peptides only)
[81:21] Quantification
[81:21] Precursors with monitored PTMs at 1% FDR: 3 out of 2213 considered
[81:21] Unmodified precursors with monitored PTM sites at 1% FDR: 1764
[81:21] Precursors with PTMs localised (when required) with > 90% confidence: 3 out of 3
[81:22] Quantification information saved to /public/local/ProteoBench/PYE_diaPASEF/B9_G_DIA_nLC_tTOF_R1.d.quant

[81:22] File #8/12
[81:22] Loading run /public/local/ProteoBench/PYE_diaPASEF/B9_G_DIA_nLC_tTOF_R2.d
[81:24] 7931928 library precursors are potentially detectable
[81:25] Calibrating with mass accuracies 20 (MS1), 20 (MS2)
[84:06] RT window set to 1.95442
[84:06] Ion mobility window set to 0.0393624
[84:06] Recommended MS1 mass accuracy setting: 12.3837 ppm
[88:54] Removing low confidence identifications
[90:41] Precursors at 1% peptidoform FDR: 8189
[90:41] Removing interfering precursors
[90:46] Training neural networks on 41854 PSMs
[90:48] Number of IDs at 0.01 FDR: 13554
[90:49] Precursors at 1% peptidoform FDR: 11245
[90:50] Calculating protein q-values
[90:50] Number of genes identified at 1% FDR: 0 (precursor-level), 0 (protein-level) (inference performed using proteotypic peptides only)
[90:50] Quantification
[90:50] Precursors with monitored PTMs at 1% FDR: 14 out of 2367 considered
[90:50] Unmodified precursors with monitored PTM sites at 1% FDR: 1851
[90:50] Precursors with PTMs localised (when required) with > 90% confidence: 9 out of 14
[90:51] Quantification information saved to /public/local/ProteoBench/PYE_diaPASEF/B9_G_DIA_nLC_tTOF_R2.d.quant

[90:51] File #9/12
[90:51] Loading run /public/local/ProteoBench/PYE_diaPASEF/B9_G_DIA_nLC_tTOF_R3.d
[90:54] 7931928 library precursors are potentially detectable
[90:55] Calibrating with mass accuracies 20 (MS1), 20 (MS2)
[93:44] RT window set to 1.77893
[93:44] Ion mobility window set to 0.0398876
[93:45] Recommended MS1 mass accuracy setting: 12.6052 ppm
[98:32] Removing low confidence identifications
[100:19] Precursors at 1% peptidoform FDR: 8325
[100:19] Removing interfering precursors
[100:24] Training neural networks on 42060 PSMs
[100:26] Number of IDs at 0.01 FDR: 13926
[100:27] Precursors at 1% peptidoform FDR: 11739
[100:28] Calculating protein q-values
[100:28] Number of genes identified at 1% FDR: 0 (precursor-level), 0 (protein-level) (inference performed using proteotypic peptides only)
[100:28] Quantification
[100:28] Precursors with monitored PTMs at 1% FDR: 88 out of 2475 considered
[100:28] Unmodified precursors with monitored PTM sites at 1% FDR: 1927
[100:28] Precursors with PTMs localised (when required) with > 90% confidence: 84 out of 88
[100:29] Quantification information saved to /public/local/ProteoBench/PYE_diaPASEF/B9_G_DIA_nLC_tTOF_R3.d.quant

[100:29] File #10/12
[100:29] Loading run /public/local/ProteoBench/PYE_diaPASEF/B9_G_DIA_nLC_tTOF_R4.d
[100:32] 7931928 library precursors are potentially detectable
[100:33] Calibrating with mass accuracies 20 (MS1), 20 (MS2)
[103:26] RT window set to 1.89609
[103:26] Ion mobility window set to 0.0400781
[103:26] Recommended MS1 mass accuracy setting: 12.8496 ppm
[108:29] Removing low confidence identifications
[110:23] Precursors at 1% peptidoform FDR: 8431
[110:24] Removing interfering precursors
[110:28] Training neural networks on 42609 PSMs
[110:30] Number of IDs at 0.01 FDR: 14011
[110:31] Precursors at 1% peptidoform FDR: 11780
[110:32] Calculating protein q-values
[110:32] Number of genes identified at 1% FDR: 0 (precursor-level), 0 (protein-level) (inference performed using proteotypic peptides only)
[110:32] Quantification
[110:33] Precursors with monitored PTMs at 1% FDR: 94 out of 2435 considered
[110:33] Unmodified precursors with monitored PTM sites at 1% FDR: 1993
[110:33] Precursors with PTMs localised (when required) with > 90% confidence: 92 out of 94
[110:33] Quantification information saved to /public/local/ProteoBench/PYE_diaPASEF/B9_G_DIA_nLC_tTOF_R4.d.quant

[110:33] File #11/12
[110:33] Loading run /public/local/ProteoBench/PYE_diaPASEF/B9_G_DIA_nLC_tTOF_R5.d
[110:36] 7931928 library precursors are potentially detectable
[110:37] Calibrating with mass accuracies 20 (MS1), 20 (MS2)
[113:29] RT window set to 2.27542
[113:29] Ion mobility window set to 0.0395539
[113:29] Recommended MS1 mass accuracy setting: 12.4611 ppm
[119:18] Removing low confidence identifications
[121:26] Precursors at 1% peptidoform FDR: 7691
[121:27] Removing interfering precursors
[121:31] Training neural networks on 42654 PSMs
[121:33] Number of IDs at 0.01 FDR: 13859
[121:34] Precursors at 1% peptidoform FDR: 11361
[121:35] Calculating protein q-values
[121:35] Number of genes identified at 1% FDR: 0 (precursor-level), 0 (protein-level) (inference performed using proteotypic peptides only)
[121:35] Quantification
[121:35] Precursors with monitored PTMs at 1% FDR: 65 out of 2398 considered
[121:35] Unmodified precursors with monitored PTM sites at 1% FDR: 1902
[121:35] Precursors with PTMs localised (when required) with > 90% confidence: 56 out of 65
[121:36] Quantification information saved to /public/local/ProteoBench/PYE_diaPASEF/B9_G_DIA_nLC_tTOF_R5.d.quant

[121:36] File #12/12
[121:36] Loading run /public/local/ProteoBench/PYE_diaPASEF/B9_G_DIA_nLC_tTOF_R6.d
[121:39] 7931928 library precursors are potentially detectable
[121:40] Calibrating with mass accuracies 20 (MS1), 20 (MS2)
[124:32] RT window set to 2.26906
[124:32] Ion mobility window set to 0.0419062
[124:32] Recommended MS1 mass accuracy setting: 13.2294 ppm
[130:25] Removing low confidence identifications
[132:40] Precursors at 1% peptidoform FDR: 8447
[132:40] Removing interfering precursors
[132:45] Training neural networks on 42739 PSMs
[132:48] Number of IDs at 0.01 FDR: 14131
[132:49] Precursors at 1% peptidoform FDR: 11967
[132:49] Calculating protein q-values
[132:50] Number of genes identified at 1% FDR: 0 (precursor-level), 0 (protein-level) (inference performed using proteotypic peptides only)
[132:50] Quantification
[132:50] Precursors with monitored PTMs at 1% FDR: 17 out of 2481 considered
[132:50] Unmodified precursors with monitored PTM sites at 1% FDR: 1894
[132:50] Precursors with PTMs localised (when required) with > 90% confidence: 14 out of 17
[132:51] Quantification information saved to /public/local/ProteoBench/PYE_diaPASEF/B9_G_DIA_nLC_tTOF_R6.d.quant

[132:51] Cross-run analysis
[132:51] Reading quantification information: 12 files
[132:53] Quantifying peptides
[133:11] Quantification parameters: 0.321305, 0.00278969, 0.0108315, 0.0226059, 0.130655, 0.0990433, 0.36039, 0.123378, 0.208543, 0.0297247, 0.124697, 0.0910701, 0.364063, 0.246713, 0.216092, 0.0420617
[133:14] Assembling protein groups
[133:16] Quantifying proteins
[133:16] Calculating q-values for protein and gene groups
[133:20] Calculating global q-values for protein and gene groups
[133:20] Protein groups with global q-value <= 0.01: 3279
[133:20] Compressed report saved to /home/robbe/PB_output/results/test_run/PYE_diaPASEF/diann_v1.9.2/report.parquet. Use R 'arrow' or Python 'PyArrow' package to process
[133:20] Writing report
[133:22] Report saved to /home/robbe/PB_output/results/test_run/PYE_diaPASEF/diann_v1.9.2/report.tsv.
[133:22] Stats report saved to /home/robbe/PB_output/results/test_run/PYE_diaPASEF/diann_v1.9.2/report.stats.tsv
[133:22] Generating spectral library:
[133:22] 20432 target and 198 decoy precursors saved
WARNING: 278 precursors without any fragments annotated were skipped
[133:22] Spectral library saved to /home/robbe/PB_output/results/test_run/PYE_diaPASEF/diann_v1.9.2/report-lib.parquet

