DIA-NN 1.9.2 (Data-Independent Acquisition by Neural Networks)
Compiled on Oct 31 2024 04:27:44
Current date and time: Sun Apr 12 17:57:44 2026
Logical CPU cores: 128
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 --lib --threads 80 --verbose 1 --out plasma_output/diann1.9.2/report.tsv --qvalue 0.01 --gen-spec-lib --predictor --fasta ProteoBenchFASTA_DDAQuantification.fasta --fasta-search --min-fr-mz 50 --max-fr-mz 2000 --met-excision --min-pep-len 6 --max-pep-len 30 --min-pr-mz 400 --max-pr-mz 1000 --min-pr-charge 1 --max-pr-charge 4 --cut K*,R* --missed-cleavages 1 --unimod4 --var-mods 1 --var-mod UniMod:35,15.994915,M --var-mod UniMod:1,42.010565,*n --peptidoforms --reanalyse --relaxed-prot-inf --rt-profiling 

Thread number set to 80
Output will be filtered at 0.01 FDR
A spectral library will be generated
Deep learning will be used to generate a new in silico spectral library from peptides provided
DIA-NN will carry out FASTA digest for in silico lib generation
Min fragment m/z set to 50
Max fragment m/z set to 2000
N-terminal methionine excision enabled
Min peptide length set to 6
Max peptide length set to 30
Min precursor m/z set to 400
Max precursor m/z set to 1000
Min precursor charge set to 1
Max precursor charge set to 4
In silico digest will involve cuts at K*,R*
Maximum number of missed cleavages set to 1
Cysteine carbamidomethylation enabled as a fixed modification
Maximum number of variable modifications set to 1
Modification UniMod:35 with mass delta 15.9949 at M will be considered as variable
Modification UniMod:1 with mass delta 42.0106 at *n will be considered as variable
Peptidoform scoring enabled
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
Heuristic protein grouping will be used, to reduce the number of protein groups obtained; this mode is recommended for benchmarking protein ID numbers, GO/pathway and system-scale analyses
The spectral library (if generated) will retain the original spectra but will include empirically-aligned RTs
DIA-NN will optimise the mass accuracy automatically using the first run in the experiment. This is useful primarily for quick initial analyses, when it is not yet known which mass accuracy setting works best for a particular acquisition scheme.
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
The following variable modifications will be scored: UniMod:35 UniMod:1 

12 files will be processed
[0:00] Loading FASTA ProteoBenchFASTA_DDAQuantification.fasta
[0:05] Processing FASTA
[0:11] Assembling elution groups
[0:17] 5116692 precursors generated
[0:17] Protein names missing for some isoforms
[0:17] Gene names missing for some isoforms
[0:17] Library contains 31685 proteins, and 0 genes
[0:24] [0:34] [3:30] [3:56] [3:59] [4:02] Saving the library to plasma_output/diann1.9.2/report-lib.predicted.speclib
[4:11] Initialising library
[4:23] Loading spectral library plasma_output/diann1.9.2/report-lib.predicted.speclib
[4:26] Library annotated with sequence database(s): ProteoBenchFASTA_DDAQuantification.fasta
[4:27] Spectral library loaded: 31837 protein isoforms, 51765 protein groups and 5116692 precursors in 2716663 elution groups.
[4:27] Loading protein annotations from FASTA ProteoBenchFASTA_DDAQuantification.fasta
[4:28] Annotating library proteins with information from the FASTA database
[4:28] Protein names missing for some isoforms
[4:28] Gene names missing for some isoforms
[4:28] Library contains 31685 proteins, and 0 genes
[4:32] Initialising library

First pass: generating a spectral library from DIA data

[4:48] File #1/12
[4:48] 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
[4:50] 5116692 library precursors are potentially detectable
[4:51] Calibrating with mass accuracies 20 (MS1), 20 (MS2)
[6:23] RT window set to 2.08706
[6:23] Ion mobility window set to 0.0399954
[6:23] Peak width: 3.188
[6:23] Scan window radius set to 7
[6:24] Recommended MS1 mass accuracy setting: 12.721 ppm
[8:17] Optimised mass accuracy: 11.8094 ppm
[10:16] Removing low confidence identifications
[10:59] Precursors at 1% peptidoform FDR: 5972
[10:59] Removing interfering precursors
[11:03] Training neural networks on 35398 PSMs
[11:05] Number of IDs at 0.01 FDR: 10750
[11:05] Precursors at 1% peptidoform FDR: 9128
[11:06] Calculating protein q-values
[11:06] Number of genes identified at 1% FDR: 0 (precursor-level), 0 (protein-level) (inference performed using proteotypic peptides only)
[11:06] Quantification
[11:06] Precursors with monitored PTMs at 1% FDR: 97 out of 2108 considered
[11:06] Unmodified precursors with monitored PTM sites at 1% FDR: 1636
[11:06] Precursors with PTMs localised (when required) with > 90% confidence: 97 out of 97
[11:07] Quantification information saved to /public/local/ProteoBench/PYE_diaPASEF/A9_G_DIA_nLC_tTOF_R1.d.quant

[11:07] File #2/12
[11:07] Loading run /public/local/ProteoBench/PYE_diaPASEF/A9_G_DIA_nLC_tTOF_R2.d
[11:09] 5116692 library precursors are potentially detectable
[11:10] Calibrating with mass accuracies 20 (MS1), 20 (MS2)
[12:39] RT window set to 1.99781
[12:39] Ion mobility window set to 0.0400598
[12:39] Recommended MS1 mass accuracy setting: 12.8807 ppm
[14:43] Removing low confidence identifications
[15:27] Precursors at 1% peptidoform FDR: 7492
[15:27] Removing interfering precursors
[15:30] Training neural networks on 39769 PSMs
[15:32] Number of IDs at 0.01 FDR: 12146
[15:33] Precursors at 1% peptidoform FDR: 10254
[15:33] Calculating protein q-values
[15:33] Number of genes identified at 1% FDR: 0 (precursor-level), 0 (protein-level) (inference performed using proteotypic peptides only)
[15:34] Quantification
[15:34] Precursors with monitored PTMs at 1% FDR: 95 out of 2382 considered
[15:34] Unmodified precursors with monitored PTM sites at 1% FDR: 1920
[15:34] Precursors with PTMs localised (when required) with > 90% confidence: 92 out of 95
[15:34] Quantification information saved to /public/local/ProteoBench/PYE_diaPASEF/A9_G_DIA_nLC_tTOF_R2.d.quant

[15:34] File #3/12
[15:34] Loading run /public/local/ProteoBench/PYE_diaPASEF/A9_G_DIA_nLC_tTOF_R3.d
[15:36] 5116692 library precursors are potentially detectable
[15:37] Calibrating with mass accuracies 20 (MS1), 20 (MS2)
[17:08] RT window set to 1.9523
[17:08] Ion mobility window set to 0.0389519
[17:08] Recommended MS1 mass accuracy setting: 12.9646 ppm
[19:07] Removing low confidence identifications
[19:47] Precursors at 1% peptidoform FDR: 7029
[19:48] Removing interfering precursors
[19:52] Training neural networks on 39133 PSMs
[19:54] Number of IDs at 0.01 FDR: 11638
[19:55] Precursors at 1% peptidoform FDR: 9820
[19:55] Calculating protein q-values
[19:56] Number of genes identified at 1% FDR: 0 (precursor-level), 0 (protein-level) (inference performed using proteotypic peptides only)
[19:56] Quantification
[19:56] Precursors with monitored PTMs at 1% FDR: 53 out of 2269 considered
[19:56] Unmodified precursors with monitored PTM sites at 1% FDR: 1750
[19:56] Precursors with PTMs localised (when required) with > 90% confidence: 52 out of 53
[19:57] Quantification information saved to /public/local/ProteoBench/PYE_diaPASEF/A9_G_DIA_nLC_tTOF_R3.d.quant

[19:57] File #4/12
[19:57] Loading run /public/local/ProteoBench/PYE_diaPASEF/A9_G_DIA_nLC_tTOF_R4.d
[19:59] 5116692 library precursors are potentially detectable
[19:59] Calibrating with mass accuracies 20 (MS1), 20 (MS2)
[21:32] RT window set to 1.77602
[21:32] Ion mobility window set to 0.0395083
[21:32] Recommended MS1 mass accuracy setting: 13.2395 ppm
[23:29] Removing low confidence identifications
[24:08] Precursors at 1% peptidoform FDR: 7262
[24:08] Removing interfering precursors
[24:12] Training neural networks on 40741 PSMs
[24:14] Number of IDs at 0.01 FDR: 12266
[24:15] Precursors at 1% peptidoform FDR: 10517
[24:15] Calculating protein q-values
[24:15] Number of genes identified at 1% FDR: 0 (precursor-level), 0 (protein-level) (inference performed using proteotypic peptides only)
[24:15] Quantification
[24:16] Precursors with monitored PTMs at 1% FDR: 34 out of 2402 considered
[24:16] Unmodified precursors with monitored PTM sites at 1% FDR: 1906
[24:16] Precursors with PTMs localised (when required) with > 90% confidence: 32 out of 34
[24:16] Quantification information saved to /public/local/ProteoBench/PYE_diaPASEF/A9_G_DIA_nLC_tTOF_R4.d.quant

[24:16] File #5/12
[24:16] Loading run /public/local/ProteoBench/PYE_diaPASEF/A9_G_DIA_nLC_tTOF_R5.d
[24:18] 5116692 library precursors are potentially detectable
[24:19] Calibrating with mass accuracies 20 (MS1), 20 (MS2)
[25:52] RT window set to 1.8118
[25:52] Ion mobility window set to 0.0380289
[25:52] Recommended MS1 mass accuracy setting: 12.9196 ppm
[27:48] Removing low confidence identifications
[28:28] Precursors at 1% peptidoform FDR: 7125
[28:28] Removing interfering precursors
[28:31] Training neural networks on 40583 PSMs
[28:33] Number of IDs at 0.01 FDR: 12267
[28:34] Precursors at 1% peptidoform FDR: 10365
[28:34] Calculating protein q-values
[28:35] Number of genes identified at 1% FDR: 0 (precursor-level), 0 (protein-level) (inference performed using proteotypic peptides only)
[28:35] Quantification
[28:35] Precursors with monitored PTMs at 1% FDR: 38 out of 2302 considered
[28:35] Unmodified precursors with monitored PTM sites at 1% FDR: 1852
[28:35] Precursors with PTMs localised (when required) with > 90% confidence: 36 out of 38
[28:35] Quantification information saved to /public/local/ProteoBench/PYE_diaPASEF/A9_G_DIA_nLC_tTOF_R5.d.quant

[28:35] File #6/12
[28:35] Loading run /public/local/ProteoBench/PYE_diaPASEF/A9_G_DIA_nLC_tTOF_R6.d
[28:38] 5116692 library precursors are potentially detectable
[28:38] Calibrating with mass accuracies 20 (MS1), 20 (MS2)
[30:09] RT window set to 1.86513
[30:09] Ion mobility window set to 0.0398792
[30:09] Recommended MS1 mass accuracy setting: 12.7073 ppm
[32:09] Removing low confidence identifications
[32:50] Precursors at 1% peptidoform FDR: 7518
[32:50] Removing interfering precursors
[32:54] Training neural networks on 39958 PSMs
[32:57] Number of IDs at 0.01 FDR: 11977
[32:58] Precursors at 1% peptidoform FDR: 10468
[32:58] Calculating protein q-values
[32:59] Number of genes identified at 1% FDR: 0 (precursor-level), 0 (protein-level) (inference performed using proteotypic peptides only)
[32:59] Quantification
[32:59] Precursors with monitored PTMs at 1% FDR: 85 out of 2374 considered
[32:59] Unmodified precursors with monitored PTM sites at 1% FDR: 1909
[32:59] Precursors with PTMs localised (when required) with > 90% confidence: 80 out of 85
[33:00] Quantification information saved to /public/local/ProteoBench/PYE_diaPASEF/A9_G_DIA_nLC_tTOF_R6.d.quant

[33:00] File #7/12
[33:00] Loading run /public/local/ProteoBench/PYE_diaPASEF/B9_G_DIA_nLC_tTOF_R1.d
[33:02] 5116692 library precursors are potentially detectable
[33:02] Calibrating with mass accuracies 20 (MS1), 20 (MS2)
[34:33] RT window set to 1.7606
[34:33] Ion mobility window set to 0.0373989
[34:33] Recommended MS1 mass accuracy setting: 12.5909 ppm
[36:19] Removing low confidence identifications
[36:55] Precursors at 1% peptidoform FDR: 6711
[36:55] Removing interfering precursors
[36:59] Training neural networks on 39192 PSMs
[37:02] Number of IDs at 0.01 FDR: 11923
[37:02] Precursors at 1% peptidoform FDR: 10389
[37:03] Calculating protein q-values
[37:03] Number of genes identified at 1% FDR: 0 (precursor-level), 0 (protein-level) (inference performed using proteotypic peptides only)
[37:03] Quantification
[37:04] Precursors with monitored PTMs at 1% FDR: 133 out of 2042 considered
[37:04] Unmodified precursors with monitored PTM sites at 1% FDR: 1651
[37:04] Precursors with PTMs localised (when required) with > 90% confidence: 131 out of 133
[37:04] Quantification information saved to /public/local/ProteoBench/PYE_diaPASEF/B9_G_DIA_nLC_tTOF_R1.d.quant

[37:04] File #8/12
[37:04] Loading run /public/local/ProteoBench/PYE_diaPASEF/B9_G_DIA_nLC_tTOF_R2.d
[37:06] 5116692 library precursors are potentially detectable
[37:07] Calibrating with mass accuracies 20 (MS1), 20 (MS2)
[38:32] RT window set to 2.31828
[38:32] Ion mobility window set to 0.0391075
[38:32] Recommended MS1 mass accuracy setting: 12.6577 ppm
[40:41] Removing low confidence identifications
[41:25] Precursors at 1% peptidoform FDR: 6799
[41:25] Removing interfering precursors
[41:28] Training neural networks on 41522 PSMs
[41:30] Number of IDs at 0.01 FDR: 12627
[41:31] Precursors at 1% peptidoform FDR: 10359
[41:32] Calculating protein q-values
[41:32] Number of genes identified at 1% FDR: 0 (precursor-level), 0 (protein-level) (inference performed using proteotypic peptides only)
[41:32] Quantification
[41:32] Precursors with monitored PTMs at 1% FDR: 153 out of 2259 considered
[41:32] Unmodified precursors with monitored PTM sites at 1% FDR: 1654
[41:32] Precursors with PTMs localised (when required) with > 90% confidence: 151 out of 153
[41:33] Quantification information saved to /public/local/ProteoBench/PYE_diaPASEF/B9_G_DIA_nLC_tTOF_R2.d.quant

[41:33] File #9/12
[41:33] Loading run /public/local/ProteoBench/PYE_diaPASEF/B9_G_DIA_nLC_tTOF_R3.d
[41:35] 5116692 library precursors are potentially detectable
[41:35] Calibrating with mass accuracies 20 (MS1), 20 (MS2)
[43:05] RT window set to 1.67865
[43:05] Ion mobility window set to 0.0399218
[43:05] Recommended MS1 mass accuracy setting: 12.8975 ppm
[44:55] Removing low confidence identifications
[45:31] Precursors at 1% peptidoform FDR: 7363
[45:31] Removing interfering precursors
[45:35] Training neural networks on 43884 PSMs
[45:37] Number of IDs at 0.01 FDR: 13063
[45:38] Precursors at 1% peptidoform FDR: 10159
[45:39] Calculating protein q-values
[45:39] Number of genes identified at 1% FDR: 0 (precursor-level), 0 (protein-level) (inference performed using proteotypic peptides only)
[45:39] Quantification
[45:39] Precursors with monitored PTMs at 1% FDR: 157 out of 2324 considered
[45:39] Unmodified precursors with monitored PTM sites at 1% FDR: 1700
[45:39] Precursors with PTMs localised (when required) with > 90% confidence: 152 out of 157
[45:40] Quantification information saved to /public/local/ProteoBench/PYE_diaPASEF/B9_G_DIA_nLC_tTOF_R3.d.quant

[45:40] File #10/12
[45:40] Loading run /public/local/ProteoBench/PYE_diaPASEF/B9_G_DIA_nLC_tTOF_R4.d
[45:42] 5116692 library precursors are potentially detectable
[45:43] Calibrating with mass accuracies 20 (MS1), 20 (MS2)
[47:14] RT window set to 1.96999
[47:14] Ion mobility window set to 0.0383005
[47:14] Recommended MS1 mass accuracy setting: 12.8762 ppm
[49:14] Removing low confidence identifications
[49:54] Precursors at 1% peptidoform FDR: 7914
[49:55] Removing interfering precursors
[49:58] Training neural networks on 43726 PSMs
[50:01] Number of IDs at 0.01 FDR: 13118
[50:02] Precursors at 1% peptidoform FDR: 10406
[50:02] Calculating protein q-values
[50:03] Number of genes identified at 1% FDR: 0 (precursor-level), 0 (protein-level) (inference performed using proteotypic peptides only)
[50:03] Quantification
[50:03] Precursors with monitored PTMs at 1% FDR: 158 out of 2321 considered
[50:03] Unmodified precursors with monitored PTM sites at 1% FDR: 1628
[50:03] Precursors with PTMs localised (when required) with > 90% confidence: 157 out of 158
[50:03] Quantification information saved to /public/local/ProteoBench/PYE_diaPASEF/B9_G_DIA_nLC_tTOF_R4.d.quant

[50:03] File #11/12
[50:03] Loading run /public/local/ProteoBench/PYE_diaPASEF/B9_G_DIA_nLC_tTOF_R5.d
[50:06] 5116692 library precursors are potentially detectable
[50:06] Calibrating with mass accuracies 20 (MS1), 20 (MS2)
[51:36] RT window set to 1.9064
[51:36] Ion mobility window set to 0.0395028
[51:36] Recommended MS1 mass accuracy setting: 13.5147 ppm
[53:33] Removing low confidence identifications
[54:13] Precursors at 1% peptidoform FDR: 7363
[54:13] Removing interfering precursors
[54:16] Training neural networks on 42076 PSMs
[54:18] Number of IDs at 0.01 FDR: 13166
[54:19] Precursors at 1% peptidoform FDR: 10520
[54:20] Calculating protein q-values
[54:20] Number of genes identified at 1% FDR: 0 (precursor-level), 0 (protein-level) (inference performed using proteotypic peptides only)
[54:20] Quantification
[54:20] Precursors with monitored PTMs at 1% FDR: 186 out of 2335 considered
[54:20] Unmodified precursors with monitored PTM sites at 1% FDR: 1704
[54:20] Precursors with PTMs localised (when required) with > 90% confidence: 184 out of 186
[54:21] Quantification information saved to /public/local/ProteoBench/PYE_diaPASEF/B9_G_DIA_nLC_tTOF_R5.d.quant

[54:21] File #12/12
[54:21] Loading run /public/local/ProteoBench/PYE_diaPASEF/B9_G_DIA_nLC_tTOF_R6.d
[54:23] 5116692 library precursors are potentially detectable
[54:24] Calibrating with mass accuracies 20 (MS1), 20 (MS2)
[55:54] RT window set to 1.85728
[55:54] Ion mobility window set to 0.0390487
[55:54] Recommended MS1 mass accuracy setting: 12.8492 ppm
[57:55] Removing low confidence identifications
[58:35] Precursors at 1% peptidoform FDR: 7968
[58:35] Removing interfering precursors
[58:39] Training neural networks on 44940 PSMs
[58:42] Number of IDs at 0.01 FDR: 13541
[58:42] Precursors at 1% peptidoform FDR: 11199
[58:43] Calculating protein q-values
[58:44] Number of genes identified at 1% FDR: 0 (precursor-level), 0 (protein-level) (inference performed using proteotypic peptides only)
[58:44] Quantification
[58:44] Precursors with monitored PTMs at 1% FDR: 162 out of 2436 considered
[58:44] Unmodified precursors with monitored PTM sites at 1% FDR: 1809
[58:44] Precursors with PTMs localised (when required) with > 90% confidence: 159 out of 162
[58:44] Quantification information saved to /public/local/ProteoBench/PYE_diaPASEF/B9_G_DIA_nLC_tTOF_R6.d.quant

[58:44] Cross-run analysis
[58:44] Reading quantification information: 12 files
[58:46] Quantifying peptides
[58:53] Assembling protein groups
[58:54] Quantifying proteins
[58:55] Calculating q-values for protein and gene groups
[58:57] Calculating global q-values for protein and gene groups
[58:57] Protein groups with global q-value <= 0.01: 2771
[58:58] Compressed report saved to plasma_output/diann1.9.2/report-first-pass.parquet. Use R 'arrow' or Python 'PyArrow' package to process
[58:58] Writing report
[59:00] Report saved to plasma_output/diann1.9.2/report-first-pass.tsv.
[59:00] Stats report saved to plasma_output/diann1.9.2/report-first-pass.stats.tsv
[59:00] Generating spectral library:
[59:01] 18975 target and 200 decoy precursors saved
[59:01] Spectral library saved to plasma_output/diann1.9.2/report-lib.parquet

[59:02] Loading spectral library plasma_output/diann1.9.2/report-lib.parquet
[59:02] Spectral library loaded: 3625 protein isoforms, 3454 protein groups and 19175 precursors in 18198 elution groups.
[59:02] Loading protein annotations from FASTA ProteoBenchFASTA_DDAQuantification.fasta
[59:02] Annotating library proteins with information from the FASTA database
[59:02] Protein names missing for some isoforms
[59:02] Gene names missing for some isoforms
[59:02] Library contains 3595 proteins, and 0 genes
[59:02] Initialising library
[59:02] Saving the library to plasma_output/diann1.9.2/report-lib.parquet.skyline.speclib


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

[59:02] File #1/12
[59:02] Loading run /public/local/ProteoBench/PYE_diaPASEF/A9_G_DIA_nLC_tTOF_R1.d
[59:04] 18975 library precursors are potentially detectable
[59:04] Calibrating with mass accuracies 20 (MS1), 20 (MS2)
[59:05] RT window set to 0.703522
[59:05] Ion mobility window set to 0.0109066
[59:05] Recommended MS1 mass accuracy setting: 12.576 ppm
[59:06] Removing low confidence identifications
[59:06] Precursors at 1% peptidoform FDR: 9047
[59:06] Removing interfering precursors
[59:06] Training neural networks on 25732 PSMs
[59:07] Number of IDs at 0.01 FDR: 14767
[59:07] Precursors at 1% peptidoform FDR: 12924
[59:07] Calculating protein q-values
[59:07] Number of genes identified at 1% FDR: 0 (precursor-level), 0 (protein-level) (inference performed using proteotypic peptides only)
[59:07] Quantification
[59:07] Precursors with monitored PTMs at 1% FDR: 156 out of 2831 considered
[59:07] Unmodified precursors with monitored PTM sites at 1% FDR: 2333
[59:07] Precursors with PTMs localised (when required) with > 90% confidence: 153 out of 156

[59:07] File #2/12
[59:07] Loading run /public/local/ProteoBench/PYE_diaPASEF/A9_G_DIA_nLC_tTOF_R2.d
[59:09] 18975 library precursors are potentially detectable
[59:09] Calibrating with mass accuracies 20 (MS1), 20 (MS2)
[59:10] RT window set to 0.713763
[59:10] Ion mobility window set to 0.0100926
[59:10] Recommended MS1 mass accuracy setting: 13.3444 ppm
[59:11] Removing low confidence identifications
[59:12] Precursors at 1% peptidoform FDR: 10319
[59:12] Removing interfering precursors
[59:12] Training neural networks on 26089 PSMs
[59:12] Number of IDs at 0.01 FDR: 15713
[59:13] Precursors at 1% peptidoform FDR: 13568
[59:13] Calculating protein q-values
[59:13] Number of genes identified at 1% FDR: 0 (precursor-level), 0 (protein-level) (inference performed using proteotypic peptides only)
[59:13] Quantification
[59:13] Precursors with monitored PTMs at 1% FDR: 165 out of 3009 considered
[59:13] Unmodified precursors with monitored PTM sites at 1% FDR: 2425
[59:13] Precursors with PTMs localised (when required) with > 90% confidence: 163 out of 165

[59:13] File #3/12
[59:13] Loading run /public/local/ProteoBench/PYE_diaPASEF/A9_G_DIA_nLC_tTOF_R3.d
[59:15] 18975 library precursors are potentially detectable
[59:15] Calibrating with mass accuracies 20 (MS1), 20 (MS2)
[59:16] RT window set to 0.69387
[59:16] Ion mobility window set to 0.0107098
[59:16] Recommended MS1 mass accuracy setting: 13.5254 ppm
[59:17] Removing low confidence identifications
[59:17] Precursors at 1% peptidoform FDR: 10076
[59:17] Removing interfering precursors
[59:17] Training neural networks on 26105 PSMs
[59:18] Number of IDs at 0.01 FDR: 15437
[59:19] Precursors at 1% peptidoform FDR: 13374
[59:19] Calculating protein q-values
[59:19] Number of genes identified at 1% FDR: 0 (precursor-level), 0 (protein-level) (inference performed using proteotypic peptides only)
[59:19] Quantification
[59:19] Precursors with monitored PTMs at 1% FDR: 151 out of 2943 considered
[59:19] Unmodified precursors with monitored PTM sites at 1% FDR: 2390
[59:19] Precursors with PTMs localised (when required) with > 90% confidence: 146 out of 151

[59:19] File #4/12
[59:19] Loading run /public/local/ProteoBench/PYE_diaPASEF/A9_G_DIA_nLC_tTOF_R4.d
[59:21] 18975 library precursors are potentially detectable
[59:21] Calibrating with mass accuracies 20 (MS1), 20 (MS2)
[59:22] RT window set to 0.696526
[59:22] Ion mobility window set to 0.0106405
[59:22] Recommended MS1 mass accuracy setting: 13.2021 ppm
[59:23] Removing low confidence identifications
[59:23] Precursors at 1% peptidoform FDR: 10616
[59:23] Removing interfering precursors
[59:23] Training neural networks on 26270 PSMs
[59:24] Number of IDs at 0.01 FDR: 15930
[59:25] Precursors at 1% peptidoform FDR: 13562
[59:25] Calculating protein q-values
[59:25] Number of genes identified at 1% FDR: 0 (precursor-level), 0 (protein-level) (inference performed using proteotypic peptides only)
[59:25] Quantification
[59:25] Precursors with monitored PTMs at 1% FDR: 164 out of 2957 considered
[59:25] Unmodified precursors with monitored PTM sites at 1% FDR: 2412
[59:25] Precursors with PTMs localised (when required) with > 90% confidence: 161 out of 164

[59:25] File #5/12
[59:25] Loading run /public/local/ProteoBench/PYE_diaPASEF/A9_G_DIA_nLC_tTOF_R5.d
[59:27] 18975 library precursors are potentially detectable
[59:27] Calibrating with mass accuracies 20 (MS1), 20 (MS2)
[59:28] RT window set to 0.700517
[59:28] Ion mobility window set to 0.0107977
[59:28] Recommended MS1 mass accuracy setting: 13.2913 ppm
[59:29] Removing low confidence identifications
[59:29] Precursors at 1% peptidoform FDR: 10322
[59:29] Removing interfering precursors
[59:29] Training neural networks on 26205 PSMs
[59:30] Number of IDs at 0.01 FDR: 15762
[59:30] Precursors at 1% peptidoform FDR: 13669
[59:30] Calculating protein q-values
[59:30] Number of genes identified at 1% FDR: 0 (precursor-level), 0 (protein-level) (inference performed using proteotypic peptides only)
[59:30] Quantification
[59:30] Precursors with monitored PTMs at 1% FDR: 164 out of 2983 considered
[59:30] Unmodified precursors with monitored PTM sites at 1% FDR: 2421
[59:30] Precursors with PTMs localised (when required) with > 90% confidence: 162 out of 164

[59:30] File #6/12
[59:30] Loading run /public/local/ProteoBench/PYE_diaPASEF/A9_G_DIA_nLC_tTOF_R6.d
[59:33] 18975 library precursors are potentially detectable
[59:33] Calibrating with mass accuracies 20 (MS1), 20 (MS2)
[59:34] RT window set to 0.691102
[59:34] Ion mobility window set to 0.0108395
[59:34] Recommended MS1 mass accuracy setting: 13.1558 ppm
[59:34] Removing low confidence identifications
[59:35] Precursors at 1% peptidoform FDR: 10404
[59:35] Removing interfering precursors
[59:35] Training neural networks on 26196 PSMs
[59:36] Number of IDs at 0.01 FDR: 15651
[59:36] Precursors at 1% peptidoform FDR: 13416
[59:36] Calculating protein q-values
[59:36] Number of genes identified at 1% FDR: 0 (precursor-level), 0 (protein-level) (inference performed using proteotypic peptides only)
[59:36] Quantification
[59:37] Precursors with monitored PTMs at 1% FDR: 165 out of 2984 considered
[59:37] Unmodified precursors with monitored PTM sites at 1% FDR: 2378
[59:37] Precursors with PTMs localised (when required) with > 90% confidence: 163 out of 165

[59:37] File #7/12
[59:37] Loading run /public/local/ProteoBench/PYE_diaPASEF/B9_G_DIA_nLC_tTOF_R1.d
[59:38] 18975 library precursors are potentially detectable
[59:38] Calibrating with mass accuracies 20 (MS1), 20 (MS2)
[59:40] RT window set to 0.698907
[59:40] Ion mobility window set to 0.0100174
[59:40] Recommended MS1 mass accuracy setting: 13.1443 ppm
[59:40] Removing low confidence identifications
[59:41] Precursors at 1% peptidoform FDR: 10318
[59:41] Removing interfering precursors
[59:41] Training neural networks on 26215 PSMs
[59:42] Number of IDs at 0.01 FDR: 15946
[59:42] Precursors at 1% peptidoform FDR: 13459
[59:42] Calculating protein q-values
[59:42] Number of genes identified at 1% FDR: 0 (precursor-level), 0 (protein-level) (inference performed using proteotypic peptides only)
[59:42] Quantification
[59:42] Precursors with monitored PTMs at 1% FDR: 173 out of 2929 considered
[59:42] Unmodified precursors with monitored PTM sites at 1% FDR: 2317
[59:42] Precursors with PTMs localised (when required) with > 90% confidence: 167 out of 173

[59:42] File #8/12
[59:42] Loading run /public/local/ProteoBench/PYE_diaPASEF/B9_G_DIA_nLC_tTOF_R2.d
[59:44] 18975 library precursors are potentially detectable
[59:44] Calibrating with mass accuracies 20 (MS1), 20 (MS2)
[59:45] RT window set to 0.694157
[59:45] Ion mobility window set to 0.01
[59:45] Recommended MS1 mass accuracy setting: 12.6579 ppm
[59:46] Removing low confidence identifications
[59:46] Precursors at 1% peptidoform FDR: 10487
[59:46] Removing interfering precursors
[59:46] Training neural networks on 26275 PSMs
[59:47] Number of IDs at 0.01 FDR: 15982
[59:48] Precursors at 1% peptidoform FDR: 13545
[59:48] Calculating protein q-values
[59:48] Number of genes identified at 1% FDR: 0 (precursor-level), 0 (protein-level) (inference performed using proteotypic peptides only)
[59:48] Quantification
[59:48] Precursors with monitored PTMs at 1% FDR: 170 out of 2958 considered
[59:48] Unmodified precursors with monitored PTM sites at 1% FDR: 2331
[59:48] Precursors with PTMs localised (when required) with > 90% confidence: 167 out of 170

[59:48] File #9/12
[59:48] Loading run /public/local/ProteoBench/PYE_diaPASEF/B9_G_DIA_nLC_tTOF_R3.d
[59:50] 18975 library precursors are potentially detectable
[59:50] Calibrating with mass accuracies 20 (MS1), 20 (MS2)
[59:51] RT window set to 0.690985
[59:51] Ion mobility window set to 0.0107277
[59:51] Recommended MS1 mass accuracy setting: 13.4834 ppm
[59:51] Removing low confidence identifications
[59:52] Precursors at 1% peptidoform FDR: 11191
[59:52] Removing interfering precursors
[59:52] Training neural networks on 26415 PSMs
[59:53] Number of IDs at 0.01 FDR: 16613
[59:53] Precursors at 1% peptidoform FDR: 13911
[59:53] Calculating protein q-values
[59:53] Number of genes identified at 1% FDR: 0 (precursor-level), 0 (protein-level) (inference performed using proteotypic peptides only)
[59:53] Quantification
[59:53] Precursors with monitored PTMs at 1% FDR: 181 out of 3079 considered
[59:53] Unmodified precursors with monitored PTM sites at 1% FDR: 2406
[59:53] Precursors with PTMs localised (when required) with > 90% confidence: 176 out of 181

[59:54] File #10/12
[59:54] Loading run /public/local/ProteoBench/PYE_diaPASEF/B9_G_DIA_nLC_tTOF_R4.d
[59:56] 18975 library precursors are potentially detectable
[59:56] Calibrating with mass accuracies 20 (MS1), 20 (MS2)
[59:57] RT window set to 0.687889
[59:57] Ion mobility window set to 0.0106457
[59:57] Recommended MS1 mass accuracy setting: 13.2859 ppm
[59:58] Removing low confidence identifications
[59:58] Precursors at 1% peptidoform FDR: 11069
[59:58] Removing interfering precursors
[59:58] Training neural networks on 26496 PSMs
[59:59] Number of IDs at 0.01 FDR: 16505
[60:00] Precursors at 1% peptidoform FDR: 13894
[60:00] Calculating protein q-values
[60:00] Number of genes identified at 1% FDR: 0 (precursor-level), 0 (protein-level) (inference performed using proteotypic peptides only)
[60:00] Quantification
[60:00] Precursors with monitored PTMs at 1% FDR: 180 out of 3049 considered
[60:00] Unmodified precursors with monitored PTM sites at 1% FDR: 2407
[60:00] Precursors with PTMs localised (when required) with > 90% confidence: 177 out of 180

[60:00] File #11/12
[60:00] Loading run /public/local/ProteoBench/PYE_diaPASEF/B9_G_DIA_nLC_tTOF_R5.d
[60:02] 18975 library precursors are potentially detectable
[60:02] Calibrating with mass accuracies 20 (MS1), 20 (MS2)
[60:03] RT window set to 0.69108
[60:03] Ion mobility window set to 0.0112415
[60:03] Recommended MS1 mass accuracy setting: 13.2705 ppm
[60:04] Removing low confidence identifications
[60:05] Precursors at 1% peptidoform FDR: 11131
[60:05] Removing interfering precursors
[60:05] Training neural networks on 26488 PSMs
[60:05] Number of IDs at 0.01 FDR: 16393
[60:06] Precursors at 1% peptidoform FDR: 13735
[60:06] Calculating protein q-values
[60:06] Number of genes identified at 1% FDR: 0 (precursor-level), 0 (protein-level) (inference performed using proteotypic peptides only)
[60:06] Quantification
[60:06] Precursors with monitored PTMs at 1% FDR: 178 out of 3030 considered
[60:06] Unmodified precursors with monitored PTM sites at 1% FDR: 2368
[60:06] Precursors with PTMs localised (when required) with > 90% confidence: 174 out of 178

[60:06] File #12/12
[60:06] Loading run /public/local/ProteoBench/PYE_diaPASEF/B9_G_DIA_nLC_tTOF_R6.d
[60:08] 18975 library precursors are potentially detectable
[60:08] Calibrating with mass accuracies 20 (MS1), 20 (MS2)
[60:09] RT window set to 0.675036
[60:09] Ion mobility window set to 0.0107662
[60:09] Recommended MS1 mass accuracy setting: 13.2597 ppm
[60:10] Removing low confidence identifications
[60:11] Precursors at 1% peptidoform FDR: 11300
[60:11] Removing interfering precursors
[60:11] Training neural networks on 26286 PSMs
[60:12] Number of IDs at 0.01 FDR: 16323
[60:12] Precursors at 1% peptidoform FDR: 13494
[60:12] Calculating protein q-values
[60:12] Number of genes identified at 1% FDR: 0 (precursor-level), 0 (protein-level) (inference performed using proteotypic peptides only)
[60:12] Quantification
[60:12] Precursors with monitored PTMs at 1% FDR: 174 out of 2920 considered
[60:12] Unmodified precursors with monitored PTM sites at 1% FDR: 2305
[60:12] Precursors with PTMs localised (when required) with > 90% confidence: 172 out of 174

[60:12] Cross-run analysis
[60:12] Reading quantification information: 12 files
[60:13] Quantifying peptides
[60:29] Quantification parameters: 0.342672, 0.00262058, 0.0127024, 0.0133214, 0.0133759, 0.0127044, 0.384616, 0.24275, 0.283612, 0.0135028, 0.0144515, 0.0140885, 0.392946, 0.327487, 0.281027, 0.0139565
[60:33] Quantifying proteins
[60:33] Calculating q-values for protein and gene groups
[60:33] Calculating global q-values for protein and gene groups
[60:33] Protein groups with global q-value <= 0.01: 2569
[60:34] Compressed report saved to plasma_output/diann1.9.2/report.parquet. Use R 'arrow' or Python 'PyArrow' package to process
[60:34] Writing report
[60:36] Report saved to plasma_output/diann1.9.2/report.tsv.
[60:36] Stats report saved to plasma_output/diann1.9.2/report.stats.tsv

