DIA-NN 1.9.1 (Data-Independent Acquisition by Neural Networks)
Compiled on Jul 15 2024 09:42:01
Current date and time: Sun Apr 12 22:10:42 2026
Logical CPU cores: 128
diann-1.9.1/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/diann1.9.1/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
[0:04] Processing FASTA
[0:09] Assembling elution groups
[0:15] 5116692 precursors generated
[0:15] Protein names missing for some isoforms
[0:15] Gene names missing for some isoforms
[0:15] Library contains 31685 proteins, and 0 genes
[0:22] [0:31] [2:30] [2:48] [2:51] [2:54] Saving the library to report-lib.predicted.speclib
[3:09] Initialising library
[3:19] Loading spectral library report-lib.predicted.speclib
[3:24] Library annotated with sequence database(s): ProteoBenchFASTA_DDAQuantification.fasta
[3:25] Spectral library loaded: 31837 protein isoforms, 51765 protein groups and 5116692 precursors in 2716663 elution groups.
[3:25] Loading protein annotations from FASTA ProteoBenchFASTA_DDAQuantification.fasta
[3:26] Annotating library proteins with information from the FASTA database
[3:26] Protein names missing for some isoforms
[3:26] Gene names missing for some isoforms
[3:26] Library contains 31685 proteins, and 0 genes
[3:33] [3:42] [5:42] [6:00] [6:05] [6:08] Saving the library to report-lib.predicted.speclib
[6:22] Initialising library

First pass: generating a spectral library from DIA data

[6:30] File #1/12
[6:30] 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
[6:32] 5116692 library precursors are potentially detectable
[6:33] Processing...
[8:32] RT window set to 2.29392
[8:32] Ion mobility window set to 0.0376571
[8:32] Peak width: 3.384
[8:32] Scan window radius set to 7
[8:33] Recommended MS1 mass accuracy setting: 12.9032 ppm
[11:28] Optimised mass accuracy: 11.995 ppm
[13:36] Removing low confidence identifications
[14:07] Precursors at 1% peptidoform FDR: 6062
[14:08] Removing interfering precursors
[14:11] Training neural networks: 23737 targets, 13409 decoys
[14:13] Number of IDs at 0.01 FDR: 11971
[14:14] Precursors at 1% peptidoform FDR: 7805
[14:14] Calculating protein q-values
[14:15] Number of genes identified at 1% FDR: 0 (precursor-level), 0 (protein-level) (inference performed using proteotypic peptides only)
[14:15] Quantification
[14:15] Precursors with monitored PTMs at 1% FDR: 36 out of 3663 considered
[14:15] Unmodified precursors with monitored PTM sites at 1% FDR: 1279
[14:15] Precursors with PTMs localised (when required) with > 90% confidence: 36 out of 36
[14:16] Quantification information saved to /public/local/ProteoBench/PYE_diaPASEF/A9_G_DIA_nLC_tTOF_R1.d.quant

[14:16] File #2/12
[14:16] Loading run /public/local/ProteoBench/PYE_diaPASEF/A9_G_DIA_nLC_tTOF_R2.d
[14:18] 5116692 library precursors are potentially detectable
[14:18] Processing...
[16:04] RT window set to 1.80369
[16:04] Ion mobility window set to 0.0382017
[16:05] Recommended MS1 mass accuracy setting: 12.9661 ppm
[18:01] Removing low confidence identifications
[18:27] Precursors at 1% peptidoform FDR: 8223
[18:27] Removing interfering precursors
[18:30] Training neural networks: 27467 targets, 15582 decoys
[18:32] Number of IDs at 0.01 FDR: 15776
[18:33] Precursors at 1% peptidoform FDR: 10032
[18:34] Calculating protein q-values
[18:34] Number of genes identified at 1% FDR: 0 (precursor-level), 0 (protein-level) (inference performed using proteotypic peptides only)
[18:34] Quantification
[18:34] Precursors with monitored PTMs at 1% FDR: 72 out of 5649 considered
[18:34] Unmodified precursors with monitored PTM sites at 1% FDR: 1704
[18:34] Precursors with PTMs localised (when required) with > 90% confidence: 72 out of 72
[18:35] Quantification information saved to /public/local/ProteoBench/PYE_diaPASEF/A9_G_DIA_nLC_tTOF_R2.d.quant

[18:35] File #3/12
[18:35] Loading run /public/local/ProteoBench/PYE_diaPASEF/A9_G_DIA_nLC_tTOF_R3.d
[18:37] 5116692 library precursors are potentially detectable
[18:38] Processing...
[20:42] RT window set to 1.67945
[20:42] Ion mobility window set to 0.0361618
[20:43] Recommended MS1 mass accuracy setting: 12.1475 ppm
[22:44] Removing low confidence identifications
[23:07] Precursors at 1% peptidoform FDR: 7252
[23:08] Removing interfering precursors
[23:11] Training neural networks: 25670 targets, 14655 decoys
[23:12] Number of IDs at 0.01 FDR: 14646
[23:13] Precursors at 1% peptidoform FDR: 9550
[23:14] Calculating protein q-values
[23:14] Number of genes identified at 1% FDR: 0 (precursor-level), 0 (protein-level) (inference performed using proteotypic peptides only)
[23:14] Quantification
[23:15] Precursors with monitored PTMs at 1% FDR: 70 out of 5135 considered
[23:15] Unmodified precursors with monitored PTM sites at 1% FDR: 1658
[23:15] Precursors with PTMs localised (when required) with > 90% confidence: 70 out of 70
[23:15] Quantification information saved to /public/local/ProteoBench/PYE_diaPASEF/A9_G_DIA_nLC_tTOF_R3.d.quant

[23:15] File #4/12
[23:15] Loading run /public/local/ProteoBench/PYE_diaPASEF/A9_G_DIA_nLC_tTOF_R4.d
[23:17] 5116692 library precursors are potentially detectable
[23:18] Processing...
[25:10] RT window set to 1.86433
[25:10] Ion mobility window set to 0.037558
[25:11] Recommended MS1 mass accuracy setting: 12.196 ppm
[27:13] Removing low confidence identifications
[27:40] Precursors at 1% peptidoform FDR: 7964
[27:40] Removing interfering precursors
[27:44] Training neural networks: 28193 targets, 15668 decoys
[27:46] Number of IDs at 0.01 FDR: 16128
[27:47] Precursors at 1% peptidoform FDR: 10358
[27:47] Calculating protein q-values
[27:48] Number of genes identified at 1% FDR: 0 (precursor-level), 0 (protein-level) (inference performed using proteotypic peptides only)
[27:48] Quantification
[27:48] Precursors with monitored PTMs at 1% FDR: 72 out of 5851 considered
[27:48] Unmodified precursors with monitored PTM sites at 1% FDR: 1808
[27:48] Precursors with PTMs localised (when required) with > 90% confidence: 71 out of 72
[27:49] Quantification information saved to /public/local/ProteoBench/PYE_diaPASEF/A9_G_DIA_nLC_tTOF_R4.d.quant

[27:49] File #5/12
[27:49] Loading run /public/local/ProteoBench/PYE_diaPASEF/A9_G_DIA_nLC_tTOF_R5.d
[27:51] 5116692 library precursors are potentially detectable
[27:52] Processing...
[29:43] RT window set to 1.73793
[29:43] Ion mobility window set to 0.0361219
[29:43] Recommended MS1 mass accuracy setting: 12.6 ppm
[31:36] Removing low confidence identifications
[32:01] Precursors at 1% peptidoform FDR: 7922
[32:02] Removing interfering precursors
[32:05] Training neural networks: 27120 targets, 15379 decoys
[32:07] Number of IDs at 0.01 FDR: 15482
[32:08] Precursors at 1% peptidoform FDR: 9743
[32:08] Calculating protein q-values
[32:09] Number of genes identified at 1% FDR: 0 (precursor-level), 0 (protein-level) (inference performed using proteotypic peptides only)
[32:09] Quantification
[32:09] Precursors with monitored PTMs at 1% FDR: 62 out of 5371 considered
[32:09] Unmodified precursors with monitored PTM sites at 1% FDR: 1641
[32:09] Precursors with PTMs localised (when required) with > 90% confidence: 60 out of 62
[32:09] Quantification information saved to /public/local/ProteoBench/PYE_diaPASEF/A9_G_DIA_nLC_tTOF_R5.d.quant

[32:10] File #6/12
[32:10] Loading run /public/local/ProteoBench/PYE_diaPASEF/A9_G_DIA_nLC_tTOF_R6.d
[32:12] 5116692 library precursors are potentially detectable
[32:12] Processing...
[34:00] RT window set to 1.83979
[34:00] Ion mobility window set to 0.0361842
[34:00] Recommended MS1 mass accuracy setting: 12.1475 ppm
[35:55] Removing low confidence identifications
[36:21] Precursors at 1% peptidoform FDR: 7794
[36:21] Removing interfering precursors
[36:24] Training neural networks: 26860 targets, 15168 decoys
[36:26] Number of IDs at 0.01 FDR: 15268
[36:27] Precursors at 1% peptidoform FDR: 9685
[36:27] Calculating protein q-values
[36:28] Number of genes identified at 1% FDR: 0 (precursor-level), 0 (protein-level) (inference performed using proteotypic peptides only)
[36:28] Quantification
[36:28] Precursors with monitored PTMs at 1% FDR: 49 out of 5323 considered
[36:28] Unmodified precursors with monitored PTM sites at 1% FDR: 1639
[36:28] Precursors with PTMs localised (when required) with > 90% confidence: 49 out of 49
[36:29] Quantification information saved to /public/local/ProteoBench/PYE_diaPASEF/A9_G_DIA_nLC_tTOF_R6.d.quant

[36:29] File #7/12
[36:29] Loading run /public/local/ProteoBench/PYE_diaPASEF/B9_G_DIA_nLC_tTOF_R1.d
[36:31] 5116692 library precursors are potentially detectable
[36:31] Processing...
[38:42] RT window set to 2.11707
[38:42] Ion mobility window set to 0.0350074
[38:43] Recommended MS1 mass accuracy setting: 12.6875 ppm
[41:01] Removing low confidence identifications
[41:28] Precursors at 1% peptidoform FDR: 7214
[41:29] Removing interfering precursors
[41:32] Training neural networks: 26044 targets, 14722 decoys
[41:34] Number of IDs at 0.01 FDR: 14977
[41:35] Precursors at 1% peptidoform FDR: 9519
[41:35] Calculating protein q-values
[41:36] Number of genes identified at 1% FDR: 0 (precursor-level), 0 (protein-level) (inference performed using proteotypic peptides only)
[41:36] Quantification
[41:36] Precursors with monitored PTMs at 1% FDR: 91 out of 4862 considered
[41:36] Unmodified precursors with monitored PTM sites at 1% FDR: 1411
[41:36] Precursors with PTMs localised (when required) with > 90% confidence: 90 out of 91
[41:37] Quantification information saved to /public/local/ProteoBench/PYE_diaPASEF/B9_G_DIA_nLC_tTOF_R1.d.quant

[41:37] File #8/12
[41:37] Loading run /public/local/ProteoBench/PYE_diaPASEF/B9_G_DIA_nLC_tTOF_R2.d
[41:39] 5116692 library precursors are potentially detectable
[41:39] Processing...
[43:25] RT window set to 1.93715
[43:25] Ion mobility window set to 0.0367912
[43:26] Recommended MS1 mass accuracy setting: 12.6912 ppm
[45:19] Removing low confidence identifications
[45:44] Precursors at 1% peptidoform FDR: 7779
[45:44] Removing interfering precursors
[45:47] Training neural networks: 28613 targets, 16155 decoys
[45:49] Number of IDs at 0.01 FDR: 16081
[45:50] Precursors at 1% peptidoform FDR: 10035
[45:50] Calculating protein q-values
[45:51] Number of genes identified at 1% FDR: 0 (precursor-level), 0 (protein-level) (inference performed using proteotypic peptides only)
[45:51] Quantification
[45:51] Precursors with monitored PTMs at 1% FDR: 78 out of 5433 considered
[45:51] Unmodified precursors with monitored PTM sites at 1% FDR: 1530
[45:51] Precursors with PTMs localised (when required) with > 90% confidence: 77 out of 78
[45:52] Quantification information saved to /public/local/ProteoBench/PYE_diaPASEF/B9_G_DIA_nLC_tTOF_R2.d.quant

[45:52] File #9/12
[45:52] Loading run /public/local/ProteoBench/PYE_diaPASEF/B9_G_DIA_nLC_tTOF_R3.d
[45:54] 5116692 library precursors are potentially detectable
[45:54] Processing...
[47:27] RT window set to 1.70776
[47:27] Ion mobility window set to 0.0355931
[47:28] Recommended MS1 mass accuracy setting: 12.3293 ppm
[49:09] Removing low confidence identifications
[49:32] Precursors at 1% peptidoform FDR: 8093
[49:33] Removing interfering precursors
[49:36] Training neural networks: 30166 targets, 17040 decoys
[49:37] Number of IDs at 0.01 FDR: 16688
[49:39] Precursors at 1% peptidoform FDR: 10370
[49:39] Calculating protein q-values
[49:40] Number of genes identified at 1% FDR: 0 (precursor-level), 0 (protein-level) (inference performed using proteotypic peptides only)
[49:40] Quantification
[49:40] Precursors with monitored PTMs at 1% FDR: 108 out of 5841 considered
[49:40] Unmodified precursors with monitored PTM sites at 1% FDR: 1540
[49:40] Precursors with PTMs localised (when required) with > 90% confidence: 106 out of 108
[49:40] Quantification information saved to /public/local/ProteoBench/PYE_diaPASEF/B9_G_DIA_nLC_tTOF_R3.d.quant

[49:41] File #10/12
[49:41] Loading run /public/local/ProteoBench/PYE_diaPASEF/B9_G_DIA_nLC_tTOF_R4.d
[49:43] 5116692 library precursors are potentially detectable
[49:43] Processing...
[51:37] RT window set to 2.11744
[51:37] Ion mobility window set to 0.0358562
[51:38] Recommended MS1 mass accuracy setting: 12.3532 ppm
[53:42] Removing low confidence identifications
[54:11] Precursors at 1% peptidoform FDR: 8578
[54:11] Removing interfering precursors
[54:14] Training neural networks: 29672 targets, 16758 decoys
[54:16] Number of IDs at 0.01 FDR: 16940
[54:17] Precursors at 1% peptidoform FDR: 10207
[54:18] Calculating protein q-values
[54:18] Number of genes identified at 1% FDR: 0 (precursor-level), 0 (protein-level) (inference performed using proteotypic peptides only)
[54:18] Quantification
[54:18] Precursors with monitored PTMs at 1% FDR: 54 out of 5902 considered
[54:18] Unmodified precursors with monitored PTM sites at 1% FDR: 1534
[54:18] Precursors with PTMs localised (when required) with > 90% confidence: 53 out of 54
[54:19] Quantification information saved to /public/local/ProteoBench/PYE_diaPASEF/B9_G_DIA_nLC_tTOF_R4.d.quant

[54:19] File #11/12
[54:19] Loading run /public/local/ProteoBench/PYE_diaPASEF/B9_G_DIA_nLC_tTOF_R5.d
[54:21] 5116692 library precursors are potentially detectable
[54:22] Processing...
[55:56] RT window set to 1.84736
[55:56] Ion mobility window set to 0.0367391
[55:56] Recommended MS1 mass accuracy setting: 11.993 ppm
[57:42] Removing low confidence identifications
[58:08] Precursors at 1% peptidoform FDR: 8264
[58:08] Removing interfering precursors
[58:11] Training neural networks: 29570 targets, 16510 decoys
[58:13] Number of IDs at 0.01 FDR: 16929
[58:14] Precursors at 1% peptidoform FDR: 10002
[58:15] Calculating protein q-values
[58:15] Number of genes identified at 1% FDR: 0 (precursor-level), 0 (protein-level) (inference performed using proteotypic peptides only)
[58:15] Quantification
[58:15] Precursors with monitored PTMs at 1% FDR: 52 out of 5779 considered
[58:15] Unmodified precursors with monitored PTM sites at 1% FDR: 1481
[58:15] Precursors with PTMs localised (when required) with > 90% confidence: 51 out of 52
[58:16] Quantification information saved to /public/local/ProteoBench/PYE_diaPASEF/B9_G_DIA_nLC_tTOF_R5.d.quant

[58:16] File #12/12
[58:16] Loading run /public/local/ProteoBench/PYE_diaPASEF/B9_G_DIA_nLC_tTOF_R6.d
[58:18] 5116692 library precursors are potentially detectable
[58:19] Processing...
[60:10] RT window set to 1.82423
[60:10] Ion mobility window set to 0.0360156
[60:10] Recommended MS1 mass accuracy setting: 12.5809 ppm
[62:10] Removing low confidence identifications
[62:35] Precursors at 1% peptidoform FDR: 8597
[62:36] Removing interfering precursors
[62:39] Training neural networks: 30662 targets, 17134 decoys
[62:41] Number of IDs at 0.01 FDR: 17407
[62:42] Precursors at 1% peptidoform FDR: 10985
[62:43] Calculating protein q-values
[62:43] Number of genes identified at 1% FDR: 0 (precursor-level), 0 (protein-level) (inference performed using proteotypic peptides only)
[62:43] Quantification
[62:43] Precursors with monitored PTMs at 1% FDR: 92 out of 5987 considered
[62:43] Unmodified precursors with monitored PTM sites at 1% FDR: 1666
[62:43] Precursors with PTMs localised (when required) with > 90% confidence: 90 out of 92
[62:44] Quantification information saved to /public/local/ProteoBench/PYE_diaPASEF/B9_G_DIA_nLC_tTOF_R6.d.quant

[62:44] Cross-run analysis
[62:44] Reading quantification information: 12 files
[62:46] Quantifying peptides
[62:53] Assembling protein groups
[62:54] Quantifying proteins
[62:54] Calculating q-values for protein and gene groups
[62:56] Calculating global q-values for protein and gene groups
[62:57] Protein groups with global q-value <= 0.01: 15892
[62:57] Compressed report saved to plasma_output/diann1.9.1/report-first-pass.parquet. Use R 'arrow' or Python 'PyArrow' package to process
[62:57] Writing report
[63:00] Report saved to plasma_output/diann1.9.1/report-first-pass.tsv.
[63:00] Stats report saved to plasma_output/diann1.9.1/report-first-pass.stats.tsv
[63:00] Generating spectral library:
[63:01] 45285 target and 473 decoy precursors saved
[63:01] Spectral library saved to report-lib.parquet

[63:03] Loading spectral library report-lib.parquet
[63:03] Spectral library loaded: 16761 protein isoforms, 16355 protein groups and 45758 precursors in 44823 elution groups.
[63:03] Loading protein annotations from FASTA ProteoBenchFASTA_DDAQuantification.fasta
[63:03] Annotating library proteins with information from the FASTA database
[63:03] Protein names missing for some isoforms
[63:03] Gene names missing for some isoforms
[63:03] Library contains 16711 proteins, and 0 genes
[63:03] Initialising library
[63:04] Saving the library to report-lib.parquet.skyline.speclib


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

[63:04] File #1/12
[63:04] Loading run /public/local/ProteoBench/PYE_diaPASEF/A9_G_DIA_nLC_tTOF_R1.d
[63:05] 45285 library precursors are potentially detectable
[63:05] Processing...
[63:08] RT window set to 0.703478
[63:08] Ion mobility window set to 0.01
[63:08] Recommended MS1 mass accuracy setting: 12.8648 ppm
[63:09] Removing low confidence identifications
[63:09] Precursors at 1% peptidoform FDR: 7097
[63:09] Removing interfering precursors
[63:09] Training neural networks: 33429 targets, 17389 decoys
[63:10] Number of IDs at 0.01 FDR: 14131
[63:11] Precursors at 1% peptidoform FDR: 11110
[63:11] Calculating protein q-values
[63:11] Number of genes identified at 1% FDR: 0 (precursor-level), 0 (protein-level) (inference performed using proteotypic peptides only)
[63:11] Quantification
[63:11] Precursors with monitored PTMs at 1% FDR: 76 out of 3074 considered
[63:11] Unmodified precursors with monitored PTM sites at 1% FDR: 1876
[63:11] Precursors with PTMs localised (when required) with > 90% confidence: 76 out of 76

[63:11] File #2/12
[63:11] Loading run /public/local/ProteoBench/PYE_diaPASEF/A9_G_DIA_nLC_tTOF_R2.d
[63:13] 45285 library precursors are potentially detectable
[63:13] Processing...
[63:15] RT window set to 0.709786
[63:15] Ion mobility window set to 0.01
[63:15] Recommended MS1 mass accuracy setting: 12.8216 ppm
[63:16] Removing low confidence identifications
[63:16] Precursors at 1% peptidoform FDR: 8186
[63:16] Removing interfering precursors
[63:16] Training neural networks: 34787 targets, 18076 decoys
[63:17] Number of IDs at 0.01 FDR: 15359
[63:18] Precursors at 1% peptidoform FDR: 11653
[63:18] Calculating protein q-values
[63:18] Number of genes identified at 1% FDR: 0 (precursor-level), 0 (protein-level) (inference performed using proteotypic peptides only)
[63:18] Quantification
[63:18] Precursors with monitored PTMs at 1% FDR: 84 out of 3716 considered
[63:18] Unmodified precursors with monitored PTM sites at 1% FDR: 1976
[63:18] Precursors with PTMs localised (when required) with > 90% confidence: 82 out of 84

[63:18] File #3/12
[63:18] Loading run /public/local/ProteoBench/PYE_diaPASEF/A9_G_DIA_nLC_tTOF_R3.d
[63:20] 45285 library precursors are potentially detectable
[63:20] Processing...
[63:22] RT window set to 0.6944
[63:22] Ion mobility window set to 0.01
[63:22] Recommended MS1 mass accuracy setting: 13.2742 ppm
[63:23] Removing low confidence identifications
[63:24] Precursors at 1% peptidoform FDR: 7717
[63:24] Removing interfering precursors
[63:24] Training neural networks: 34739 targets, 18044 decoys
[63:25] Number of IDs at 0.01 FDR: 14591
[63:25] Precursors at 1% peptidoform FDR: 11328
[63:25] Calculating protein q-values
[63:25] Number of genes identified at 1% FDR: 0 (precursor-level), 0 (protein-level) (inference performed using proteotypic peptides only)
[63:25] Quantification
[63:25] Precursors with monitored PTMs at 1% FDR: 81 out of 3391 considered
[63:25] Unmodified precursors with monitored PTM sites at 1% FDR: 1916
[63:25] Precursors with PTMs localised (when required) with > 90% confidence: 79 out of 81

[63:26] File #4/12
[63:26] Loading run /public/local/ProteoBench/PYE_diaPASEF/A9_G_DIA_nLC_tTOF_R4.d
[63:28] 45285 library precursors are potentially detectable
[63:28] Processing...
[63:30] RT window set to 0.698426
[63:30] Ion mobility window set to 0.01
[63:30] Recommended MS1 mass accuracy setting: 14.5932 ppm
[63:31] Removing low confidence identifications
[63:31] Precursors at 1% peptidoform FDR: 7965
[63:31] Removing interfering precursors
[63:32] Training neural networks: 35177 targets, 18305 decoys
[63:32] Number of IDs at 0.01 FDR: 15475
[63:33] Precursors at 1% peptidoform FDR: 11780
[63:33] Calculating protein q-values
[63:33] Number of genes identified at 1% FDR: 0 (precursor-level), 0 (protein-level) (inference performed using proteotypic peptides only)
[63:33] Quantification
[63:33] Precursors with monitored PTMs at 1% FDR: 87 out of 3625 considered
[63:33] Unmodified precursors with monitored PTM sites at 1% FDR: 2009
[63:33] Precursors with PTMs localised (when required) with > 90% confidence: 86 out of 87

[63:33] File #5/12
[63:33] Loading run /public/local/ProteoBench/PYE_diaPASEF/A9_G_DIA_nLC_tTOF_R5.d
[63:35] 45285 library precursors are potentially detectable
[63:35] Processing...
[63:37] RT window set to 0.707145
[63:37] Ion mobility window set to 0.01
[63:37] Recommended MS1 mass accuracy setting: 13.4151 ppm
[63:38] Removing low confidence identifications
[63:39] Precursors at 1% peptidoform FDR: 7772
[63:39] Removing interfering precursors
[63:39] Training neural networks: 35316 targets, 18333 decoys
[63:40] Number of IDs at 0.01 FDR: 14668
[63:40] Precursors at 1% peptidoform FDR: 11548
[63:40] Calculating protein q-values
[63:40] Number of genes identified at 1% FDR: 0 (precursor-level), 0 (protein-level) (inference performed using proteotypic peptides only)
[63:40] Quantification
[63:41] Precursors with monitored PTMs at 1% FDR: 83 out of 3009 considered
[63:41] Unmodified precursors with monitored PTM sites at 1% FDR: 1921
[63:41] Precursors with PTMs localised (when required) with > 90% confidence: 82 out of 83

[63:41] File #6/12
[63:41] Loading run /public/local/ProteoBench/PYE_diaPASEF/A9_G_DIA_nLC_tTOF_R6.d
[63:43] 45285 library precursors are potentially detectable
[63:43] Processing...
[63:45] RT window set to 0.691588
[63:45] Ion mobility window set to 0.01
[63:45] Recommended MS1 mass accuracy setting: 13.4378 ppm
[63:46] Removing low confidence identifications
[63:46] Precursors at 1% peptidoform FDR: 7333
[63:46] Removing interfering precursors
[63:46] Training neural networks: 34979 targets, 18136 decoys
[63:47] Number of IDs at 0.01 FDR: 14549
[63:48] Precursors at 1% peptidoform FDR: 11387
[63:48] Calculating protein q-values
[63:48] Number of genes identified at 1% FDR: 0 (precursor-level), 0 (protein-level) (inference performed using proteotypic peptides only)
[63:48] Quantification
[63:48] Precursors with monitored PTMs at 1% FDR: 82 out of 3135 considered
[63:48] Unmodified precursors with monitored PTM sites at 1% FDR: 1915
[63:48] Precursors with PTMs localised (when required) with > 90% confidence: 81 out of 82

[63:48] File #7/12
[63:48] Loading run /public/local/ProteoBench/PYE_diaPASEF/B9_G_DIA_nLC_tTOF_R1.d
[63:50] 45285 library precursors are potentially detectable
[63:50] Processing...
[63:51] RT window set to 0.712916
[63:51] Ion mobility window set to 0.01
[63:51] Recommended MS1 mass accuracy setting: 12.819 ppm
[63:53] Removing low confidence identifications
[63:53] Precursors at 1% peptidoform FDR: 7965
[63:53] Removing interfering precursors
[63:53] Training neural networks: 34703 targets, 18022 decoys
[63:54] Number of IDs at 0.01 FDR: 15587
[63:55] Precursors at 1% peptidoform FDR: 11812
[63:55] Calculating protein q-values
[63:55] Number of genes identified at 1% FDR: 0 (precursor-level), 0 (protein-level) (inference performed using proteotypic peptides only)
[63:55] Quantification
[63:55] Precursors with monitored PTMs at 1% FDR: 80 out of 3441 considered
[63:55] Unmodified precursors with monitored PTM sites at 1% FDR: 1920
[63:55] Precursors with PTMs localised (when required) with > 90% confidence: 79 out of 80

[63:55] File #8/12
[63:55] Loading run /public/local/ProteoBench/PYE_diaPASEF/B9_G_DIA_nLC_tTOF_R2.d
[63:57] 45285 library precursors are potentially detectable
[63:57] Processing...
[63:58] RT window set to 0.686779
[63:58] Ion mobility window set to 0.01029
[63:58] Recommended MS1 mass accuracy setting: 12.8996 ppm
[64:00] Removing low confidence identifications
[64:00] Precursors at 1% peptidoform FDR: 8253
[64:00] Removing interfering precursors
[64:00] Training neural networks: 34919 targets, 18154 decoys
[64:01] Number of IDs at 0.01 FDR: 15647
[64:02] Precursors at 1% peptidoform FDR: 11884
[64:02] Calculating protein q-values
[64:02] Number of genes identified at 1% FDR: 0 (precursor-level), 0 (protein-level) (inference performed using proteotypic peptides only)
[64:02] Quantification
[64:02] Precursors with monitored PTMs at 1% FDR: 84 out of 3380 considered
[64:02] Unmodified precursors with monitored PTM sites at 1% FDR: 1913
[64:02] Precursors with PTMs localised (when required) with > 90% confidence: 83 out of 84

[64:02] File #9/12
[64:02] Loading run /public/local/ProteoBench/PYE_diaPASEF/B9_G_DIA_nLC_tTOF_R3.d
[64:04] 45285 library precursors are potentially detectable
[64:04] Processing...
[64:06] RT window set to 0.686433
[64:06] Ion mobility window set to 0.01
[64:06] Recommended MS1 mass accuracy setting: 13.1319 ppm
[64:07] Removing low confidence identifications
[64:07] Precursors at 1% peptidoform FDR: 8268
[64:07] Removing interfering precursors
[64:07] Training neural networks: 35534 targets, 18528 decoys
[64:08] Number of IDs at 0.01 FDR: 16463
[64:09] Precursors at 1% peptidoform FDR: 12175
[64:09] Calculating protein q-values
[64:09] Number of genes identified at 1% FDR: 0 (precursor-level), 0 (protein-level) (inference performed using proteotypic peptides only)
[64:09] Quantification
[64:09] Precursors with monitored PTMs at 1% FDR: 81 out of 3688 considered
[64:09] Unmodified precursors with monitored PTM sites at 1% FDR: 1971
[64:09] Precursors with PTMs localised (when required) with > 90% confidence: 80 out of 81

[64:09] File #10/12
[64:09] Loading run /public/local/ProteoBench/PYE_diaPASEF/B9_G_DIA_nLC_tTOF_R4.d
[64:11] 45285 library precursors are potentially detectable
[64:11] Processing...
[64:13] RT window set to 0.691424
[64:13] Ion mobility window set to 0.01
[64:13] Recommended MS1 mass accuracy setting: 12.5765 ppm
[64:14] Removing low confidence identifications
[64:14] Precursors at 1% peptidoform FDR: 8455
[64:14] Removing interfering precursors
[64:14] Training neural networks: 35594 targets, 18482 decoys
[64:15] Number of IDs at 0.01 FDR: 16057
[64:16] Precursors at 1% peptidoform FDR: 12033
[64:16] Calculating protein q-values
[64:16] Number of genes identified at 1% FDR: 0 (precursor-level), 0 (protein-level) (inference performed using proteotypic peptides only)
[64:16] Quantification
[64:16] Precursors with monitored PTMs at 1% FDR: 87 out of 3621 considered
[64:16] Unmodified precursors with monitored PTM sites at 1% FDR: 1953
[64:16] Precursors with PTMs localised (when required) with > 90% confidence: 86 out of 87

[64:16] File #11/12
[64:16] Loading run /public/local/ProteoBench/PYE_diaPASEF/B9_G_DIA_nLC_tTOF_R5.d
[64:18] 45285 library precursors are potentially detectable
[64:18] Processing...
[64:20] RT window set to 0.691699
[64:20] Ion mobility window set to 0.01
[64:20] Recommended MS1 mass accuracy setting: 12.7837 ppm
[64:21] Removing low confidence identifications
[64:21] Precursors at 1% peptidoform FDR: 8494
[64:21] Removing interfering precursors
[64:21] Training neural networks: 35088 targets, 18174 decoys
[64:22] Number of IDs at 0.01 FDR: 16159
[64:23] Precursors at 1% peptidoform FDR: 12025
[64:23] Calculating protein q-values
[64:23] Number of genes identified at 1% FDR: 0 (precursor-level), 0 (protein-level) (inference performed using proteotypic peptides only)
[64:23] Quantification
[64:23] Precursors with monitored PTMs at 1% FDR: 86 out of 3550 considered
[64:23] Unmodified precursors with monitored PTM sites at 1% FDR: 1934
[64:23] Precursors with PTMs localised (when required) with > 90% confidence: 85 out of 86

[64:23] File #12/12
[64:23] Loading run /public/local/ProteoBench/PYE_diaPASEF/B9_G_DIA_nLC_tTOF_R6.d
[64:25] 45285 library precursors are potentially detectable
[64:25] Processing...
[64:27] RT window set to 0.672962
[64:27] Ion mobility window set to 0.01
[64:27] Recommended MS1 mass accuracy setting: 14.2149 ppm
[64:28] Removing low confidence identifications
[64:28] Precursors at 1% peptidoform FDR: 8446
[64:29] Removing interfering precursors
[64:29] Training neural networks: 35193 targets, 18248 decoys
[64:30] Number of IDs at 0.01 FDR: 16116
[64:30] Precursors at 1% peptidoform FDR: 11981
[64:30] Calculating protein q-values
[64:30] Number of genes identified at 1% FDR: 0 (precursor-level), 0 (protein-level) (inference performed using proteotypic peptides only)
[64:30] Quantification
[64:30] Precursors with monitored PTMs at 1% FDR: 85 out of 3531 considered
[64:30] Unmodified precursors with monitored PTM sites at 1% FDR: 1914
[64:30] Precursors with PTMs localised (when required) with > 90% confidence: 84 out of 85

[64:30] Cross-run analysis
[64:30] Reading quantification information: 12 files
[64:31] Quantifying peptides
[64:54] Quantification parameters: 0.331882, 0.0027297, 0.0130764, 0.013091, 0.0135463, 0.0142583, 0.380002, 0.213996, 0.270461, 0.0137162, 0.0145999, 0.0143184, 0.319869, 0.229083, 0.192287, 0.0135635
[64:58] Quantifying proteins
[64:58] Calculating q-values for protein and gene groups
[64:58] Calculating global q-values for protein and gene groups
[64:58] Protein groups with global q-value <= 0.01: 3787
[64:59] Compressed report saved to plasma_output/diann1.9.1/report.parquet. Use R 'arrow' or Python 'PyArrow' package to process
[64:59] Writing report
[65:01] Report saved to plasma_output/diann1.9.1/report.tsv.
[65:01] Stats report saved to plasma_output/diann1.9.1/report.stats.tsv

