DIA-NN 1.9.2 (Data-Independent Acquisition by Neural Networks)
Compiled on Oct 31 2024 04:27:44
Current date and time: Thu Oct  9 10:14:44 2025
Logical CPU cores: 128
/usr/diann/1.9.2_20241031/diann-linux --fasta-search --fasta /misc/fasta/p34486_ProteoBenchFASTA_DDAQuantification_noecoli.fasta --f 20231123_DIA_240k_20Th_40ms_FAIMSCV-48_gas3p8_200pg_50pg_H_Y_r1.mzML --f 20231123_DIA_240k_20Th_40ms_FAIMSCV-48_gas3p8_240pg_10pg_H_Y_r3.mzML --f 20231123_DIA_240k_20Th_40ms_FAIMSCV-48_gas3p8_200pg_50pg_H_Y_r3.mzML --f 20231123_DIA_240k_20Th_40ms_FAIMSCV-48_gas3p8_200pg_50pg_H_Y_r2.mzML --f 20231123_DIA_240k_20Th_40ms_FAIMSCV-48_gas3p8_240pg_10pg_H_Y_r2.mzML --f 20231123_DIA_240k_20Th_40ms_FAIMSCV-48_gas3p8_240pg_10pg_H_Y_r1.mzML --threads 64 --qvalue 0.01 --matrices --predictor --met-excision --cut K*,R* --min-pep-len 6 --max-pep-len 30 --smart-profiling --var-mods 1 --var-mod UniMod:35,15.994915,M --min-pr-charge 2 --max-pr-charge 3 --min-pr-mz 400 --max-pr-mz 1500 --verbose 1 --unimod4 --missed-cleavages 1 --mass-acc 20 --mass-acc-ms1 15 --reanalyse --pg-level 0 --out-lib out-DIANN/WU334894_report-lib.tsv --out-lib-copy --temp temp-DIANN --out out-DIANN/WU334894_report.tsv 

DIA-NN will carry out FASTA digest for in silico lib generation
Thread number set to 64
Output will be filtered at 0.01 FDR
Precursor/protein x samples expression level matrices will be saved along with the main report
Deep learning will be used to generate a new in silico spectral library from peptides provided
N-terminal methionine excision enabled
In silico digest will involve cuts at K*,R*
Min peptide length set to 6
Max peptide length set to 30
When generating a spectral library, in silico predicted spectra will be retained if deemed more reliable than experimental ones
Maximum number of variable modifications set to 1
Modification UniMod:35 with mass delta 15.9949 at M will be considered as variable
Min precursor charge set to 2
Max precursor charge set to 3
Min precursor m/z set to 400
Max precursor m/z set to 1500
Cysteine carbamidomethylation enabled as a fixed modification
Maximum number of missed cleavages set to 1
A spectral library will be created from the DIA runs and used to reanalyse them; .quant files will only be saved to disk during the first step
Implicit protein grouping: isoform IDs; this determines which peptides are considered 'proteotypic' and thus affects protein FDR calculation
Copies of the spectral library and the FASTA database will be saved along with the final report
Mass accuracy will be fixed to 2e-05 (MS2) and 1.5e-05 (MS1)
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 

6 files will be processed
[0:00] Loading FASTA /misc/fasta/p34486_ProteoBenchFASTA_DDAQuantification_noecoli.fasta
[0:05] Processing FASTA
[0:12] Assembling elution groups
[0:17] 3659934 precursors generated
[0:17] Gene names missing for some isoforms
[0:17] Library contains 27293 proteins, and 0 genes
[0:23] [0:37] [4:36] [5:27] [5:38] [5:40] Saving the library to out-DIANN/WU334894_report-lib.predicted.speclib
[5:49] Initialising library
[6:00] Loading spectral library out-DIANN/WU334894_report-lib.predicted.speclib
[6:04] Library annotated with sequence database(s): /misc/fasta/p34486_ProteoBenchFASTA_DDAQuantification_noecoli.fasta
[6:05] Spectral library loaded: 27443 protein isoforms, 38000 protein groups and 3659934 precursors in 2208986 elution groups.
[6:05] Loading protein annotations from FASTA /misc/fasta/p34486_ProteoBenchFASTA_DDAQuantification_noecoli.fasta
[6:06] Annotating library proteins with information from the FASTA database
[6:06] Gene names missing for some isoforms
[6:06] Library contains 27293 proteins, and 0 genes
[6:09] Initialising library

First pass: generating a spectral library from DIA data

[6:25] File #1/6
[6:25] Loading run 20231123_DIA_240k_20Th_40ms_FAIMSCV-48_gas3p8_200pg_50pg_H_Y_r1.mzML
[6:34] 2191100 library precursors are potentially detectable
[6:35] Calibrating with mass accuracies 30 (MS1), 20 (MS2)
[7:27] RT window set to 1.15835
[7:27] Peak width: 3.028
[7:27] Scan window radius set to 6
[7:27] Recommended MS1 mass accuracy setting: 1.94226 ppm
[8:53] Removing low confidence identifications
[9:22] Precursors at 1% peptidoform FDR: 22304
[9:23] Removing interfering precursors
[9:25] Training neural networks on 99891 PSMs
[9:30] Number of IDs at 0.01 FDR: 33073
[9:32] Precursors at 1% peptidoform FDR: 28289
[9:33] Calculating protein q-values
[9:33] Number of protein isoforms identified at 1% FDR: 7344 (precursor-level), 6845 (protein-level) (inference performed using proteotypic peptides only)
[9:33] Quantification
[9:34] Precursors with monitored PTMs at 1% FDR: 47 out of 6298 considered
[9:34] Unmodified precursors with monitored PTM sites at 1% FDR: 5338
[9:34] Precursors with PTMs localised (when required) with > 90% confidence: 37 out of 47
[9:34] Quantification information saved to temp-DIANN/20231123_DIA_240k_20Th_40ms_FAIMSCV-48_gas3p8_200pg_50pg_H_Y_r1_mzML.quant

[9:34] File #2/6
[9:34] Loading run 20231123_DIA_240k_20Th_40ms_FAIMSCV-48_gas3p8_240pg_10pg_H_Y_r3.mzML
[9:42] 2191100 library precursors are potentially detectable
[9:42] Calibrating with mass accuracies 30 (MS1), 20 (MS2)
[10:17] RT window set to 1.20526
[10:17] Recommended MS1 mass accuracy setting: 2.47628 ppm
[11:32] Removing low confidence identifications
[12:03] Precursors at 1% peptidoform FDR: 21879
[12:03] Removing interfering precursors
[12:06] Training neural networks on 94703 PSMs
[12:09] Number of IDs at 0.01 FDR: 32360
[12:10] Precursors at 1% peptidoform FDR: 27256
[12:11] Calculating protein q-values
[12:11] Number of protein isoforms identified at 1% FDR: 6827 (precursor-level), 6236 (protein-level) (inference performed using proteotypic peptides only)
[12:11] Quantification
[12:11] Precursors with monitored PTMs at 1% FDR: 252 out of 6632 considered
[12:11] Unmodified precursors with monitored PTM sites at 1% FDR: 5284
[12:11] Precursors with PTMs localised (when required) with > 90% confidence: 233 out of 252
[12:12] Quantification information saved to temp-DIANN/20231123_DIA_240k_20Th_40ms_FAIMSCV-48_gas3p8_240pg_10pg_H_Y_r3_mzML.quant

[12:12] File #3/6
[12:12] Loading run 20231123_DIA_240k_20Th_40ms_FAIMSCV-48_gas3p8_200pg_50pg_H_Y_r3.mzML
[12:21] 2191100 library precursors are potentially detectable
[12:22] Calibrating with mass accuracies 30 (MS1), 20 (MS2)
[13:10] RT window set to 1.25059
[13:11] Recommended MS1 mass accuracy setting: 1.97966 ppm
[15:20] Removing low confidence identifications
[16:21] Precursors at 1% peptidoform FDR: 23160
[16:21] Removing interfering precursors
[16:29] Training neural networks on 103166 PSMs
[16:33] Number of IDs at 0.01 FDR: 34707
[16:34] Precursors at 1% peptidoform FDR: 29401
[16:35] Calculating protein q-values
[16:35] Number of protein isoforms identified at 1% FDR: 7527 (precursor-level), 7022 (protein-level) (inference performed using proteotypic peptides only)
[16:35] Quantification
[16:35] Precursors with monitored PTMs at 1% FDR: 1119 out of 7166 considered
[16:35] Unmodified precursors with monitored PTM sites at 1% FDR: 4944
[16:35] Precursors with PTMs localised (when required) with > 90% confidence: 1106 out of 1119
[16:36] Quantification information saved to temp-DIANN/20231123_DIA_240k_20Th_40ms_FAIMSCV-48_gas3p8_200pg_50pg_H_Y_r3_mzML.quant

[16:36] File #4/6
[16:36] Loading run 20231123_DIA_240k_20Th_40ms_FAIMSCV-48_gas3p8_200pg_50pg_H_Y_r2.mzML
[16:43] 2191100 library precursors are potentially detectable
[16:43] Calibrating with mass accuracies 30 (MS1), 20 (MS2)
[17:23] RT window set to 1.21359
[17:23] Recommended MS1 mass accuracy setting: 2.16181 ppm
[19:13] Removing low confidence identifications
[19:57] Precursors at 1% peptidoform FDR: 23977
[19:57] Removing interfering precursors
[20:00] Training neural networks on 105727 PSMs
[20:06] Number of IDs at 0.01 FDR: 35535
[20:09] Precursors at 1% peptidoform FDR: 29915
[20:10] Calculating protein q-values
[20:10] Number of protein isoforms identified at 1% FDR: 7623 (precursor-level), 6988 (protein-level) (inference performed using proteotypic peptides only)
[20:10] Quantification
[20:11] Precursors with monitored PTMs at 1% FDR: 843 out of 7311 considered
[20:11] Unmodified precursors with monitored PTM sites at 1% FDR: 5072
[20:11] Precursors with PTMs localised (when required) with > 90% confidence: 823 out of 843
[20:11] Quantification information saved to temp-DIANN/20231123_DIA_240k_20Th_40ms_FAIMSCV-48_gas3p8_200pg_50pg_H_Y_r2_mzML.quant

[20:11] File #5/6
[20:11] Loading run 20231123_DIA_240k_20Th_40ms_FAIMSCV-48_gas3p8_240pg_10pg_H_Y_r2.mzML
[20:22] 2191100 library precursors are potentially detectable
[20:23] Calibrating with mass accuracies 30 (MS1), 20 (MS2)
[21:13] RT window set to 1.26823
[21:13] Recommended MS1 mass accuracy setting: 1.94512 ppm
[22:52] Removing low confidence identifications
[23:34] Precursors at 1% peptidoform FDR: 21577
[23:35] Removing interfering precursors
[23:38] Training neural networks on 94743 PSMs
[23:42] Number of IDs at 0.01 FDR: 32251
[23:45] Precursors at 1% peptidoform FDR: 27671
[23:46] Calculating protein q-values
[23:47] Number of protein isoforms identified at 1% FDR: 6822 (precursor-level), 6302 (protein-level) (inference performed using proteotypic peptides only)
[23:47] Quantification
[23:48] Precursors with monitored PTMs at 1% FDR: 278 out of 6544 considered
[23:48] Unmodified precursors with monitored PTM sites at 1% FDR: 5335
[23:48] Precursors with PTMs localised (when required) with > 90% confidence: 259 out of 278
[23:49] Quantification information saved to temp-DIANN/20231123_DIA_240k_20Th_40ms_FAIMSCV-48_gas3p8_240pg_10pg_H_Y_r2_mzML.quant

[23:49] File #6/6
[23:49] Loading run 20231123_DIA_240k_20Th_40ms_FAIMSCV-48_gas3p8_240pg_10pg_H_Y_r1.mzML
[23:58] 2191100 library precursors are potentially detectable
[23:58] Calibrating with mass accuracies 30 (MS1), 20 (MS2)
[24:43] RT window set to 1.21272
[24:43] Recommended MS1 mass accuracy setting: 1.7864 ppm
[26:20] Removing low confidence identifications
[26:59] Precursors at 1% peptidoform FDR: 21424
[26:59] Removing interfering precursors
[27:02] Training neural networks on 94158 PSMs
[27:07] Number of IDs at 0.01 FDR: 31768
[27:09] Precursors at 1% peptidoform FDR: 27816
[27:09] Calculating protein q-values
[27:10] Number of protein isoforms identified at 1% FDR: 6692 (precursor-level), 6248 (protein-level) (inference performed using proteotypic peptides only)
[27:10] Quantification
[27:10] Precursors with monitored PTMs at 1% FDR: 219 out of 6459 considered
[27:10] Unmodified precursors with monitored PTM sites at 1% FDR: 5472
[27:10] Precursors with PTMs localised (when required) with > 90% confidence: 198 out of 219
[27:10] Quantification information saved to temp-DIANN/20231123_DIA_240k_20Th_40ms_FAIMSCV-48_gas3p8_240pg_10pg_H_Y_r1_mzML.quant

[27:11] Cross-run analysis
[27:11] Reading quantification information: 6 files
[27:16] Quantifying peptides
[27:28] Assembling protein groups
[27:32] Quantifying proteins
[27:32] Calculating q-values for protein and gene groups
[27:34] Calculating global q-values for protein and gene groups
[27:34] Protein groups with global q-value <= 0.01: 8856
[27:35] Compressed report saved to out-DIANN/WU334894_report-first-pass.parquet. Use R 'arrow' or Python 'PyArrow' package to process
[27:35] Writing report
[27:43] Report saved to out-DIANN/WU334894_report-first-pass.tsv.
[27:43] Saving precursor levels matrix
[27:44] Precursor levels matrix (1% precursor and protein group FDR) saved to out-DIANN/WU334894_report-first-pass.pr_matrix.tsv.
[27:44] Manifest saved to out-DIANN/WU334894_report-first-pass.manifest.txt
[27:44] Stats report saved to out-DIANN/WU334894_report-first-pass.stats.tsv
[27:48] Generating spectral library:
[27:49] 43003 target and 436 decoy precursors saved
[27:49] Spectral library saved to out-DIANN/WU334894_report-lib.parquet

[27:50] Loading spectral library out-DIANN/WU334894_report-lib.parquet
[27:50] Spectral library loaded: 9612 protein isoforms, 9760 protein groups and 43439 precursors in 42803 elution groups.
[27:50] Loading protein annotations from FASTA /misc/fasta/p34486_ProteoBenchFASTA_DDAQuantification_noecoli.fasta
[27:51] Annotating library proteins with information from the FASTA database
[27:51] Gene names missing for some isoforms
[27:51] Library contains 9588 proteins, and 0 genes
[27:51] Initialising library
[27:51] Saving the library to out-DIANN/WU334894_report-lib.parquet.skyline.speclib


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

[27:52] File #1/6
[27:52] Loading run 20231123_DIA_240k_20Th_40ms_FAIMSCV-48_gas3p8_200pg_50pg_H_Y_r1.mzML
[27:59] 43003 library precursors are potentially detectable
[27:59] Calibrating with mass accuracies 30 (MS1), 20 (MS2)
[28:01] RT window set to 0.45296
[28:01] Recommended MS1 mass accuracy setting: 2.16638 ppm
[28:04] Removing low confidence identifications
[28:07] Precursors at 1% peptidoform FDR: 26726
[28:07] Removing interfering precursors
[28:08] Training neural networks on 59970 PSMs
[28:11] Number of IDs at 0.01 FDR: 36729
[28:13] Precursors at 1% peptidoform FDR: 30725
[28:13] Calculating protein q-values
[28:13] Number of protein isoforms identified at 1% FDR: 7655 (precursor-level), 7262 (protein-level) (inference performed using proteotypic peptides only)
[28:13] Quantification
[28:14] Precursors with monitored PTMs at 1% FDR: 449 out of 7236 considered
[28:14] Unmodified precursors with monitored PTM sites at 1% FDR: 5679
[28:14] Precursors with PTMs localised (when required) with > 90% confidence: 436 out of 449

[28:14] File #2/6
[28:14] Loading run 20231123_DIA_240k_20Th_40ms_FAIMSCV-48_gas3p8_240pg_10pg_H_Y_r3.mzML
[28:21] 43003 library precursors are potentially detectable
[28:21] Calibrating with mass accuracies 30 (MS1), 20 (MS2)
[28:22] RT window set to 0.454432
[28:22] Recommended MS1 mass accuracy setting: 2.18261 ppm
[28:24] Removing low confidence identifications
[28:26] Precursors at 1% peptidoform FDR: 25728
[28:26] Removing interfering precursors
[28:27] Training neural networks on 58807 PSMs
[28:29] Number of IDs at 0.01 FDR: 35543
[28:32] Precursors at 1% peptidoform FDR: 30051
[28:32] Calculating protein q-values
[28:32] Number of protein isoforms identified at 1% FDR: 7204 (precursor-level), 6759 (protein-level) (inference performed using proteotypic peptides only)
[28:32] Quantification
[28:32] Precursors with monitored PTMs at 1% FDR: 531 out of 7246 considered
[28:32] Unmodified precursors with monitored PTM sites at 1% FDR: 5654
[28:32] Precursors with PTMs localised (when required) with > 90% confidence: 510 out of 531

[28:32] File #3/6
[28:32] Loading run 20231123_DIA_240k_20Th_40ms_FAIMSCV-48_gas3p8_200pg_50pg_H_Y_r3.mzML
[28:40] 43003 library precursors are potentially detectable
[28:40] Calibrating with mass accuracies 30 (MS1), 20 (MS2)
[28:41] RT window set to 0.4531
[28:41] Recommended MS1 mass accuracy setting: 2.27646 ppm
[28:42] Removing low confidence identifications
[28:43] Precursors at 1% peptidoform FDR: 27237
[28:44] Removing interfering precursors
[28:44] Training neural networks on 60133 PSMs
[28:45] Number of IDs at 0.01 FDR: 37004
[28:47] Precursors at 1% peptidoform FDR: 30910
[28:47] Calculating protein q-values
[28:47] Number of protein isoforms identified at 1% FDR: 7679 (precursor-level), 7179 (protein-level) (inference performed using proteotypic peptides only)
[28:47] Quantification
[28:47] Precursors with monitored PTMs at 1% FDR: 782 out of 7398 considered
[28:47] Unmodified precursors with monitored PTM sites at 1% FDR: 5492
[28:47] Precursors with PTMs localised (when required) with > 90% confidence: 770 out of 782

[28:47] File #4/6
[28:47] Loading run 20231123_DIA_240k_20Th_40ms_FAIMSCV-48_gas3p8_200pg_50pg_H_Y_r2.mzML
[28:51] 43003 library precursors are potentially detectable
[28:51] Calibrating with mass accuracies 30 (MS1), 20 (MS2)
[28:52] RT window set to 0.45492
[28:52] Recommended MS1 mass accuracy setting: 2.26037 ppm
[28:54] Removing low confidence identifications
[28:55] Precursors at 1% peptidoform FDR: 27636
[28:55] Removing interfering precursors
[28:56] Training neural networks on 60345 PSMs
[28:57] Number of IDs at 0.01 FDR: 37731
[28:59] Precursors at 1% peptidoform FDR: 31203
[28:59] Calculating protein q-values
[29:00] Number of protein isoforms identified at 1% FDR: 7753 (precursor-level), 7274 (protein-level) (inference performed using proteotypic peptides only)
[29:00] Quantification
[29:00] Precursors with monitored PTMs at 1% FDR: 792 out of 7506 considered
[29:00] Unmodified precursors with monitored PTM sites at 1% FDR: 5629
[29:00] Precursors with PTMs localised (when required) with > 90% confidence: 774 out of 792

[29:00] File #5/6
[29:00] Loading run 20231123_DIA_240k_20Th_40ms_FAIMSCV-48_gas3p8_240pg_10pg_H_Y_r2.mzML
[29:06] 43003 library precursors are potentially detectable
[29:06] Calibrating with mass accuracies 30 (MS1), 20 (MS2)
[29:08] RT window set to 0.455371
[29:09] Recommended MS1 mass accuracy setting: 2.05159 ppm
[29:10] Removing low confidence identifications
[29:12] Precursors at 1% peptidoform FDR: 26030
[29:12] Removing interfering precursors
[29:13] Training neural networks on 58883 PSMs
[29:16] Number of IDs at 0.01 FDR: 35364
[29:17] Precursors at 1% peptidoform FDR: 30078
[29:17] Calculating protein q-values
[29:17] Number of protein isoforms identified at 1% FDR: 7185 (precursor-level), 6692 (protein-level) (inference performed using proteotypic peptides only)
[29:17] Quantification
[29:18] Precursors with monitored PTMs at 1% FDR: 506 out of 7247 considered
[29:18] Unmodified precursors with monitored PTM sites at 1% FDR: 5648
[29:18] Precursors with PTMs localised (when required) with > 90% confidence: 488 out of 506

[29:18] File #6/6
[29:18] Loading run 20231123_DIA_240k_20Th_40ms_FAIMSCV-48_gas3p8_240pg_10pg_H_Y_r1.mzML
[29:23] 43003 library precursors are potentially detectable
[29:23] Calibrating with mass accuracies 30 (MS1), 20 (MS2)
[29:24] RT window set to 0.455795
[29:24] Recommended MS1 mass accuracy setting: 2.12463 ppm
[29:26] Removing low confidence identifications
[29:28] Precursors at 1% peptidoform FDR: 26058
[29:28] Removing interfering precursors
[29:29] Training neural networks on 58853 PSMs
[29:32] Number of IDs at 0.01 FDR: 35436
[29:35] Precursors at 1% peptidoform FDR: 29857
[29:35] Calculating protein q-values
[29:35] Number of protein isoforms identified at 1% FDR: 7147 (precursor-level), 6805 (protein-level) (inference performed using proteotypic peptides only)
[29:35] Quantification
[29:35] Precursors with monitored PTMs at 1% FDR: 380 out of 7120 considered
[29:35] Unmodified precursors with monitored PTM sites at 1% FDR: 5687
[29:35] Precursors with PTMs localised (when required) with > 90% confidence: 361 out of 380

[29:35] Cross-run analysis
[29:35] Reading quantification information: 6 files
[29:36] Quantifying peptides
[30:15] Quantification parameters: 0.321405, 0.002379, 0.0124909, 0.0126596, 0.0126917, 0.0126274, 0.28943, 0.166285, 0.178149, 0.0136063, 0.0148935, 0.0142687, 0.332538, 0.107964, 0.158954, 0.0125787
[30:20] Quantifying proteins
[30:20] Calculating q-values for protein and gene groups
[30:20] Calculating global q-values for protein and gene groups
[30:20] Protein groups with global q-value <= 0.01: 8778
[30:21] Compressed report saved to out-DIANN/WU334894_report.parquet. Use R 'arrow' or Python 'PyArrow' package to process
[30:21] Writing report
[30:27] Report saved to out-DIANN/WU334894_report.tsv.
[30:27] Saving precursor levels matrix
[30:27] Precursor levels matrix (1% precursor and protein group FDR) saved to out-DIANN/WU334894_report.pr_matrix.tsv.
[30:27] Saving protein group levels matrix
[30:27] Protein group levels matrix (1% precursor FDR and protein group FDR) saved to out-DIANN/WU334894_report.pg_matrix.tsv.
[30:27] Saving gene group levels matrix
[30:27] Gene groups levels matrix (1% precursor FDR and protein group FDR) saved to out-DIANN/WU334894_report.gg_matrix.tsv.
[30:27] Saving unique genes levels matrix
[30:27] Unique genes levels matrix (1% precursor FDR and protein group FDR) saved to out-DIANN/WU334894_report.unique_genes_matrix.tsv.
[30:27] Manifest saved to out-DIANN/WU334894_report.manifest.txt
[30:27] Stats report saved to out-DIANN/WU334894_report.stats.tsv

