DIA-NN 1.9.2 (Data-Independent Acquisition by Neural Networks)
Compiled on Oct 17 2024 21:58:43
Current date and time: Tue Jan 14 22:42:28 2025
CPU: GenuineIntel 13th Gen Intel(R) Core(TM) i9-13900F
SIMD instructions: AVX AVX2 FMA SSE4.1 SSE4.2 
Logical CPU cores: 32
diann.exe --f D:\Proteobench_manuscript_data\Raw_diaPASEF\Marie_2025\ttSCP_diaPASEF_Condition_A_Sample_Alpha_01_11494.d  --f D:\Proteobench_manuscript_data\Raw_diaPASEF\Marie_2025\ttSCP_diaPASEF_Condition_A_Sample_Alpha_02_11500.d  --f D:\Proteobench_manuscript_data\Raw_diaPASEF\Marie_2025\ttSCP_diaPASEF_Condition_A_Sample_Alpha_03_11506.d  --f D:\Proteobench_manuscript_data\Raw_diaPASEF\Marie_2025\ttSCP_diaPASEF_Condition_B_Sample_Alpha_01_11496.d  --f D:\Proteobench_manuscript_data\Raw_diaPASEF\Marie_2025\ttSCP_diaPASEF_Condition_B_Sample_Alpha_02_11502.d  --f D:\Proteobench_manuscript_data\Raw_diaPASEF\Marie_2025\ttSCP_diaPASEF_Condition_B_Sample_Alpha_03_11508.d  --lib  --threads 24 --verbose 1 --out D:\Proteobench_manuscript_data\run_output_diaPASEF\diann_1.9.2_default\report.tsv --qvalue 0.01 --matrices --out-lib C:\DIA-NN\1.9.2\report-lib.parquet --gen-spec-lib --predictor --fasta D:\Proteobench_manuscript_data\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 24
Output will be filtered at 0.01 FDR
Precursor/protein x samples expression level matrices will be saved along with the main report
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 

6 files will be processed
[0:00] Loading FASTA D:\Proteobench_manuscript_data\ProteoBenchFASTA_DDAQuantification.fasta
[0:03] Processing FASTA
[0:08] Assembling elution groups
[0:12] 5116692 precursors generated
[0:12] Protein names missing for some isoforms
[0:12] Gene names missing for some isoforms
[0:12] Library contains 31685 proteins, and 0 genes
[0:15] [0:19] [9:08] [10:28] [10:32] [10:32] Saving the library to C:\DIA-NN\1.9.2\report-lib.predicted.speclib
[10:41] Initialising library
[10:50] Loading spectral library C:\DIA-NN\1.9.2\report-lib.predicted.speclib
[10:53] Library annotated with sequence database(s): D:\Proteobench_manuscript_data\ProteoBenchFASTA_DDAQuantification.fasta
[10:54] Spectral library loaded: 31837 protein isoforms, 51765 protein groups and 5116692 precursors in 2716663 elution groups.
[10:54] Loading protein annotations from FASTA D:\Proteobench_manuscript_data\ProteoBenchFASTA_DDAQuantification.fasta
[10:54] Annotating library proteins with information from the FASTA database
[10:54] Protein names missing for some isoforms
[10:54] Gene names missing for some isoforms
[10:54] Library contains 31685 proteins, and 0 genes
[10:58] Initialising library

First pass: generating a spectral library from DIA data

[11:07] File #1/6
[11:07] Loading run D:\Proteobench_manuscript_data\Raw_diaPASEF\Marie_2025\ttSCP_diaPASEF_Condition_A_Sample_Alpha_01_11494.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
[12:45] 5116692 library precursors are potentially detectable
[12:49] Calibrating with mass accuracies 20 (MS1), 20 (MS2)
[17:15] RT window set to 2.51371
[17:15] Ion mobility window set to 0.0431607
[17:15] Peak width: 4.052
[17:15] Scan window radius set to 8
[17:15] Recommended MS1 mass accuracy setting: 13.8504 ppm
[21:22] Optimised mass accuracy: 11.5251 ppm
[44:29] Removing low confidence identifications
[55:11] Precursors at 1% peptidoform FDR: 62101
[55:12] Removing interfering precursors
[55:16] Training neural networks on 254323 PSMs
[55:23] Number of IDs at 0.01 FDR: 87129
[55:26] Precursors at 1% peptidoform FDR: 71202
[55:26] Calculating protein q-values
[55:26] Number of genes identified at 1% FDR: 0 (precursor-level), 0 (protein-level) (inference performed using proteotypic peptides only)
[55:26] Quantification
[55:27] Precursors with monitored PTMs at 1% FDR: 963 out of 18283 considered
[55:27] Unmodified precursors with monitored PTM sites at 1% FDR: 13697
[55:27] Precursors with PTMs localised (when required) with > 90% confidence: 947 out of 963
[55:31] Quantification information saved to D:\Proteobench_manuscript_data\Raw_diaPASEF\Marie_2025\ttSCP_diaPASEF_Condition_A_Sample_Alpha_01_11494.d.quant

[55:31] File #2/6
[55:31] Loading run D:\Proteobench_manuscript_data\Raw_diaPASEF\Marie_2025\ttSCP_diaPASEF_Condition_A_Sample_Alpha_02_11500.d
[57:02] 5116692 library precursors are potentially detectable
[57:05] Calibrating with mass accuracies 20 (MS1), 20 (MS2)
[60:36] RT window set to 2.54362
[60:36] Ion mobility window set to 0.0436509
[60:36] Recommended MS1 mass accuracy setting: 14.3672 ppm
[83:20] Removing low confidence identifications
[94:18] Precursors at 1% peptidoform FDR: 63523
[94:20] Removing interfering precursors
[94:23] Training neural networks on 257641 PSMs
[94:30] Number of IDs at 0.01 FDR: 88704
[94:33] Precursors at 1% peptidoform FDR: 73099
[94:34] Calculating protein q-values
[94:34] Number of genes identified at 1% FDR: 0 (precursor-level), 0 (protein-level) (inference performed using proteotypic peptides only)
[94:34] Quantification
[94:34] Precursors with monitored PTMs at 1% FDR: 772 out of 18148 considered
[94:34] Unmodified precursors with monitored PTM sites at 1% FDR: 14174
[94:35] Precursors with PTMs localised (when required) with > 90% confidence: 756 out of 772
[94:36] Quantification information saved to D:\Proteobench_manuscript_data\Raw_diaPASEF\Marie_2025\ttSCP_diaPASEF_Condition_A_Sample_Alpha_02_11500.d.quant

[94:36] File #3/6
[94:36] Loading run D:\Proteobench_manuscript_data\Raw_diaPASEF\Marie_2025\ttSCP_diaPASEF_Condition_A_Sample_Alpha_03_11506.d
[96:10] 5116692 library precursors are potentially detectable
[96:13] Calibrating with mass accuracies 20 (MS1), 20 (MS2)
[99:47] RT window set to 2.49499
[99:47] Ion mobility window set to 0.0426057
[99:47] Recommended MS1 mass accuracy setting: 14.6923 ppm
[122:08] Removing low confidence identifications
[132:55] Precursors at 1% peptidoform FDR: 64311
[132:57] Removing interfering precursors
[133:00] Training neural networks on 259305 PSMs
[133:07] Number of IDs at 0.01 FDR: 90824
[133:10] Precursors at 1% peptidoform FDR: 73993
[133:10] Calculating protein q-values
[133:11] Number of genes identified at 1% FDR: 0 (precursor-level), 0 (protein-level) (inference performed using proteotypic peptides only)
[133:11] Quantification
[133:11] Precursors with monitored PTMs at 1% FDR: 927 out of 18918 considered
[133:11] Unmodified precursors with monitored PTM sites at 1% FDR: 14616
[133:11] Precursors with PTMs localised (when required) with > 90% confidence: 911 out of 927
[133:13] Quantification information saved to D:\Proteobench_manuscript_data\Raw_diaPASEF\Marie_2025\ttSCP_diaPASEF_Condition_A_Sample_Alpha_03_11506.d.quant

[133:13] File #4/6
[133:13] Loading run D:\Proteobench_manuscript_data\Raw_diaPASEF\Marie_2025\ttSCP_diaPASEF_Condition_B_Sample_Alpha_01_11496.d
[134:45] 5116692 library precursors are potentially detectable
[134:48] Calibrating with mass accuracies 20 (MS1), 20 (MS2)
[138:16] RT window set to 2.20156
[138:16] Ion mobility window set to 0.0414363
[138:16] Recommended MS1 mass accuracy setting: 14.3894 ppm
[158:08] Removing low confidence identifications
[167:54] Precursors at 1% peptidoform FDR: 62747
[167:55] Removing interfering precursors
[167:59] Training neural networks on 249575 PSMs
[168:06] Number of IDs at 0.01 FDR: 86218
[168:09] Precursors at 1% peptidoform FDR: 71473
[168:09] Calculating protein q-values
[168:10] Number of genes identified at 1% FDR: 0 (precursor-level), 0 (protein-level) (inference performed using proteotypic peptides only)
[168:10] Quantification
[168:10] Precursors with monitored PTMs at 1% FDR: 683 out of 17821 considered
[168:10] Unmodified precursors with monitored PTM sites at 1% FDR: 14326
[168:10] Precursors with PTMs localised (when required) with > 90% confidence: 670 out of 683
[168:11] Quantification information saved to D:\Proteobench_manuscript_data\Raw_diaPASEF\Marie_2025\ttSCP_diaPASEF_Condition_B_Sample_Alpha_01_11496.d.quant

[168:11] File #5/6
[168:11] Loading run D:\Proteobench_manuscript_data\Raw_diaPASEF\Marie_2025\ttSCP_diaPASEF_Condition_B_Sample_Alpha_02_11502.d
[169:41] 5116692 library precursors are potentially detectable
[169:45] Calibrating with mass accuracies 20 (MS1), 20 (MS2)
[173:19] RT window set to 2.05966
[173:19] Ion mobility window set to 0.0402678
[173:19] Recommended MS1 mass accuracy setting: 13.8001 ppm
[192:24] Removing low confidence identifications
[201:32] Precursors at 1% peptidoform FDR: 63445
[201:33] Removing interfering precursors
[201:37] Training neural networks on 254935 PSMs
[201:44] Number of IDs at 0.01 FDR: 87222
[201:47] Precursors at 1% peptidoform FDR: 71745
[201:47] Calculating protein q-values
[201:47] Number of genes identified at 1% FDR: 0 (precursor-level), 0 (protein-level) (inference performed using proteotypic peptides only)
[201:47] Quantification
[201:48] Precursors with monitored PTMs at 1% FDR: 670 out of 18142 considered
[201:48] Unmodified precursors with monitored PTM sites at 1% FDR: 14184
[201:48] Precursors with PTMs localised (when required) with > 90% confidence: 653 out of 670
[201:52] Quantification information saved to D:\Proteobench_manuscript_data\Raw_diaPASEF\Marie_2025\ttSCP_diaPASEF_Condition_B_Sample_Alpha_02_11502.d.quant

[201:52] File #6/6
[201:52] Loading run D:\Proteobench_manuscript_data\Raw_diaPASEF\Marie_2025\ttSCP_diaPASEF_Condition_B_Sample_Alpha_03_11508.d
[203:30] 5116692 library precursors are potentially detectable
[203:33] Calibrating with mass accuracies 20 (MS1), 20 (MS2)
[207:07] RT window set to 1.89941
[207:07] Ion mobility window set to 0.0413312
[207:07] Recommended MS1 mass accuracy setting: 14.3761 ppm
[224:54] Removing low confidence identifications
[233:35] Precursors at 1% peptidoform FDR: 63866
[233:37] Removing interfering precursors
[233:40] Training neural networks on 253487 PSMs
[233:47] Number of IDs at 0.01 FDR: 88039
[233:50] Precursors at 1% peptidoform FDR: 72671
[233:50] Calculating protein q-values
[233:51] Number of genes identified at 1% FDR: 0 (precursor-level), 0 (protein-level) (inference performed using proteotypic peptides only)
[233:51] Quantification
[233:51] Precursors with monitored PTMs at 1% FDR: 677 out of 18237 considered
[233:51] Unmodified precursors with monitored PTM sites at 1% FDR: 14584
[233:51] Precursors with PTMs localised (when required) with > 90% confidence: 658 out of 677
[233:56] Quantification information saved to D:\Proteobench_manuscript_data\Raw_diaPASEF\Marie_2025\ttSCP_diaPASEF_Condition_B_Sample_Alpha_03_11508.d.quant

[233:56] Cross-run analysis
[233:56] Reading quantification information: 6 files
[234:02] Quantifying peptides
[234:09] Assembling protein groups
[234:10] Quantifying proteins
[234:10] Calculating q-values for protein and gene groups
[234:11] Calculating global q-values for protein and gene groups
[234:11] Protein groups with global q-value <= 0.01: 10917
[234:13] Compressed report saved to D:\Proteobench_manuscript_data\run_output_diaPASEF\diann_1.9.2_default\report-first-pass.parquet. Use R 'arrow' or Python 'PyArrow' package to process
[234:13] Writing report
[234:25] Report saved to D:\Proteobench_manuscript_data\run_output_diaPASEF\diann_1.9.2_default\report-first-pass.tsv.
[234:25] Saving precursor levels matrix
[234:25] Precursor levels matrix (1% precursor and protein group FDR) saved to D:\Proteobench_manuscript_data\run_output_diaPASEF\diann_1.9.2_default\report-first-pass.pr_matrix.tsv.
[234:25] Manifest saved to D:\Proteobench_manuscript_data\run_output_diaPASEF\diann_1.9.2_default\report-first-pass.manifest.txt
[234:25] Stats report saved to D:\Proteobench_manuscript_data\run_output_diaPASEF\diann_1.9.2_default\report-first-pass.stats.tsv
[234:25] Generating spectral library:
[234:26] 110948 target and 1129 decoy precursors saved
[234:26] Spectral library saved to C:\DIA-NN\1.9.2\report-lib.parquet

[234:26] Loading spectral library C:\DIA-NN\1.9.2\report-lib.parquet
[234:27] Spectral library loaded: 12943 protein isoforms, 12766 protein groups and 112077 precursors in 104421 elution groups.
[234:27] Loading protein annotations from FASTA D:\Proteobench_manuscript_data\ProteoBenchFASTA_DDAQuantification.fasta
[234:28] Annotating library proteins with information from the FASTA database
[234:28] Gene names missing for some isoforms
[234:28] Library contains 12926 proteins, and 0 genes
[234:28] Initialising library
[234:28] Saving the library to C:\DIA-NN\1.9.2\report-lib.parquet.skyline.speclib


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

[234:28] File #1/6
[234:28] Loading run D:\Proteobench_manuscript_data\Raw_diaPASEF\Marie_2025\ttSCP_diaPASEF_Condition_A_Sample_Alpha_01_11494.d
[236:07] 110948 library precursors are potentially detectable
[236:07] Calibrating with mass accuracies 20 (MS1), 20 (MS2)
[236:12] RT window set to 0.941324
[236:12] Ion mobility window set to 0.01
[236:12] Recommended MS1 mass accuracy setting: 14.0354 ppm
[236:27] Removing low confidence identifications
[236:37] Precursors at 1% peptidoform FDR: 73945
[236:38] Removing interfering precursors
[236:38] Training neural networks on 152731 PSMs
[236:42] Number of IDs at 0.01 FDR: 100643
[236:45] Precursors at 1% peptidoform FDR: 81402
[236:45] Calculating protein q-values
[236:45] Number of genes identified at 1% FDR: 0 (precursor-level), 0 (protein-level) (inference performed using proteotypic peptides only)
[236:45] Quantification
[236:46] Precursors with monitored PTMs at 1% FDR: 1040 out of 20952 considered
[236:46] Unmodified precursors with monitored PTM sites at 1% FDR: 16140
[236:46] Precursors with PTMs localised (when required) with > 90% confidence: 1027 out of 1040

[236:46] File #2/6
[236:46] Loading run D:\Proteobench_manuscript_data\Raw_diaPASEF\Marie_2025\ttSCP_diaPASEF_Condition_A_Sample_Alpha_02_11500.d
[238:15] 110948 library precursors are potentially detectable
[238:15] Calibrating with mass accuracies 20 (MS1), 20 (MS2)
[238:21] RT window set to 0.941166
[238:21] Ion mobility window set to 0.01
[238:21] Recommended MS1 mass accuracy setting: 14.0933 ppm
[238:36] Removing low confidence identifications
[238:46] Precursors at 1% peptidoform FDR: 76237
[238:47] Removing interfering precursors
[238:47] Training neural networks on 153519 PSMs
[238:51] Number of IDs at 0.01 FDR: 101474
[238:54] Precursors at 1% peptidoform FDR: 82319
[238:54] Calculating protein q-values
[238:54] Number of genes identified at 1% FDR: 0 (precursor-level), 0 (protein-level) (inference performed using proteotypic peptides only)
[238:54] Quantification
[238:54] Precursors with monitored PTMs at 1% FDR: 1067 out of 21102 considered
[238:54] Unmodified precursors with monitored PTM sites at 1% FDR: 16354
[238:54] Precursors with PTMs localised (when required) with > 90% confidence: 1056 out of 1067

[238:55] File #3/6
[238:55] Loading run D:\Proteobench_manuscript_data\Raw_diaPASEF\Marie_2025\ttSCP_diaPASEF_Condition_A_Sample_Alpha_03_11506.d
[240:27] 110948 library precursors are potentially detectable
[240:27] Calibrating with mass accuracies 20 (MS1), 20 (MS2)
[240:32] RT window set to 0.939773
[240:32] Ion mobility window set to 0.01
[240:32] Recommended MS1 mass accuracy setting: 14.3013 ppm
[240:47] Removing low confidence identifications
[240:58] Precursors at 1% peptidoform FDR: 77610
[240:58] Removing interfering precursors
[240:59] Training neural networks on 153224 PSMs
[241:03] Number of IDs at 0.01 FDR: 101417
[241:06] Precursors at 1% peptidoform FDR: 82430
[241:06] Calculating protein q-values
[241:06] Number of genes identified at 1% FDR: 0 (precursor-level), 0 (protein-level) (inference performed using proteotypic peptides only)
[241:06] Quantification
[241:06] Precursors with monitored PTMs at 1% FDR: 1063 out of 21230 considered
[241:06] Unmodified precursors with monitored PTM sites at 1% FDR: 16384
[241:06] Precursors with PTMs localised (when required) with > 90% confidence: 1052 out of 1063

[241:07] File #4/6
[241:07] Loading run D:\Proteobench_manuscript_data\Raw_diaPASEF\Marie_2025\ttSCP_diaPASEF_Condition_B_Sample_Alpha_01_11496.d
[242:37] 110948 library precursors are potentially detectable
[242:37] Calibrating with mass accuracies 20 (MS1), 20 (MS2)
[242:43] RT window set to 0.940599
[242:43] Ion mobility window set to 0.01
[242:43] Recommended MS1 mass accuracy setting: 13.8783 ppm
[242:58] Removing low confidence identifications
[243:08] Precursors at 1% peptidoform FDR: 76712
[243:08] Removing interfering precursors
[243:09] Training neural networks on 152807 PSMs
[243:13] Number of IDs at 0.01 FDR: 101680
[243:16] Precursors at 1% peptidoform FDR: 82820
[243:16] Calculating protein q-values
[243:16] Number of genes identified at 1% FDR: 0 (precursor-level), 0 (protein-level) (inference performed using proteotypic peptides only)
[243:16] Quantification
[243:16] Precursors with monitored PTMs at 1% FDR: 1067 out of 21344 considered
[243:16] Unmodified precursors with monitored PTM sites at 1% FDR: 16539
[243:16] Precursors with PTMs localised (when required) with > 90% confidence: 1055 out of 1067

[243:17] File #5/6
[243:17] Loading run D:\Proteobench_manuscript_data\Raw_diaPASEF\Marie_2025\ttSCP_diaPASEF_Condition_B_Sample_Alpha_02_11502.d
[244:45] 110948 library precursors are potentially detectable
[244:45] Calibrating with mass accuracies 20 (MS1), 20 (MS2)
[244:51] RT window set to 0.941058
[244:51] Ion mobility window set to 0.01
[244:51] Recommended MS1 mass accuracy setting: 14.3067 ppm
[245:06] Removing low confidence identifications
[245:16] Precursors at 1% peptidoform FDR: 77772
[245:16] Removing interfering precursors
[245:17] Training neural networks on 153393 PSMs
[245:21] Number of IDs at 0.01 FDR: 102226
[245:24] Precursors at 1% peptidoform FDR: 82995
[245:24] Calculating protein q-values
[245:24] Number of genes identified at 1% FDR: 0 (precursor-level), 0 (protein-level) (inference performed using proteotypic peptides only)
[245:24] Quantification
[245:24] Precursors with monitored PTMs at 1% FDR: 1026 out of 21511 considered
[245:24] Unmodified precursors with monitored PTM sites at 1% FDR: 16521
[245:24] Precursors with PTMs localised (when required) with > 90% confidence: 1016 out of 1026

[245:25] File #6/6
[245:25] Loading run D:\Proteobench_manuscript_data\Raw_diaPASEF\Marie_2025\ttSCP_diaPASEF_Condition_B_Sample_Alpha_03_11508.d
[247:01] 110948 library precursors are potentially detectable
[247:01] Calibrating with mass accuracies 20 (MS1), 20 (MS2)
[247:06] RT window set to 0.940309
[247:06] Ion mobility window set to 0.01
[247:06] Recommended MS1 mass accuracy setting: 14.0902 ppm
[247:21] Removing low confidence identifications
[247:32] Precursors at 1% peptidoform FDR: 77156
[247:32] Removing interfering precursors
[247:33] Training neural networks on 153442 PSMs
[247:37] Number of IDs at 0.01 FDR: 102116
[247:40] Precursors at 1% peptidoform FDR: 83337
[247:40] Calculating protein q-values
[247:40] Number of genes identified at 1% FDR: 0 (precursor-level), 0 (protein-level) (inference performed using proteotypic peptides only)
[247:40] Quantification
[247:40] Precursors with monitored PTMs at 1% FDR: 1061 out of 21441 considered
[247:40] Unmodified precursors with monitored PTM sites at 1% FDR: 16615
[247:40] Precursors with PTMs localised (when required) with > 90% confidence: 1050 out of 1061

[247:41] Cross-run analysis
[247:41] Reading quantification information: 6 files
[247:42] Quantifying peptides
[249:10] Quantification parameters: 0.342036, 0.00146879, 0.00377145, 0.0318072, 0.0619567, 0.0493292, 0.228391, 0.0142568, 0.0481525, 0.0538016, 0.0665767, 0.063125, 0.229289, 0.122277, 0.139644, 0.0115526
[249:14] Quantifying proteins
[249:14] Calculating q-values for protein and gene groups
[249:15] Calculating global q-values for protein and gene groups
[249:15] Protein groups with global q-value <= 0.01: 10975
[249:16] Compressed report saved to D:\Proteobench_manuscript_data\run_output_diaPASEF\diann_1.9.2_default\report.parquet. Use R 'arrow' or Python 'PyArrow' package to process
[249:16] Writing report
[249:30] Report saved to D:\Proteobench_manuscript_data\run_output_diaPASEF\diann_1.9.2_default\report.tsv.
[249:30] Saving precursor levels matrix
[249:31] Precursor levels matrix (1% precursor and protein group FDR) saved to D:\Proteobench_manuscript_data\run_output_diaPASEF\diann_1.9.2_default\report.pr_matrix.tsv.
[249:31] Saving protein group levels matrix
[249:31] Protein group levels matrix (1% precursor FDR and protein group FDR) saved to D:\Proteobench_manuscript_data\run_output_diaPASEF\diann_1.9.2_default\report.pg_matrix.tsv.
[249:31] Saving gene group levels matrix
[249:31] Gene groups levels matrix (1% precursor FDR and protein group FDR) saved to D:\Proteobench_manuscript_data\run_output_diaPASEF\diann_1.9.2_default\report.gg_matrix.tsv.
[249:31] Saving unique genes levels matrix
[249:31] Unique genes levels matrix (1% precursor FDR and protein group FDR) saved to D:\Proteobench_manuscript_data\run_output_diaPASEF\diann_1.9.2_default\report.unique_genes_matrix.tsv.
[249:31] Manifest saved to D:\Proteobench_manuscript_data\run_output_diaPASEF\diann_1.9.2_default\report.manifest.txt
[249:31] Stats report saved to D:\Proteobench_manuscript_data\run_output_diaPASEF\diann_1.9.2_default\report.stats.tsv

