DIA-NN 1.9.1 (Data-Independent Acquisition by Neural Networks)
Compiled on Jul 15 2024 15:40:36
Current date and time: Wed Jan 15 10:48:05 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_1.9.1_default\report.tsv --qvalue 0.01 --matrices --out-lib D:\Proteobench_manuscript_data\run_output_diaPASEF\diann_1.9.1_default\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
[0:09] Assembling elution groups
[0:13] 5116692 precursors generated
[0:13] Protein names missing for some isoforms
[0:13] Gene names missing for some isoforms
[0:13] Library contains 31685 proteins, and 0 genes
[0:15] [0:20] [13:02] [14:18] [14:22] [14:23] Saving the library to D:\Proteobench_manuscript_data\run_output_diaPASEF\diann_1.9.1_default\report-lib.predicted.speclib
[14:44] Initialising library
[14:53] Loading spectral library D:\Proteobench_manuscript_data\run_output_diaPASEF\diann_1.9.1_default\report-lib.predicted.speclib
[15:04] Library annotated with sequence database(s): D:\Proteobench_manuscript_data\ProteoBenchFASTA_DDAQuantification.fasta
[15:06] Spectral library loaded: 31837 protein isoforms, 51765 protein groups and 5116692 precursors in 2716663 elution groups.
[15:06] Loading protein annotations from FASTA D:\Proteobench_manuscript_data\ProteoBenchFASTA_DDAQuantification.fasta
[15:06] Annotating library proteins with information from the FASTA database
[15:06] Protein names missing for some isoforms
[15:06] Gene names missing for some isoforms
[15:06] Library contains 31685 proteins, and 0 genes
[15:09] [15:15] [23:36] [24:50] [24:54] [24:55] Saving the library to D:\Proteobench_manuscript_data\run_output_diaPASEF\diann_1.9.1_default\report-lib.predicted.speclib
[25:16] Initialising library

First pass: generating a spectral library from DIA data

[25:22] File #1/6
[25:22] 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
[26:56] 5116692 library precursors are potentially detectable
[26:59] Processing...
[29:12] RT window set to 1.72075
[29:12] Ion mobility window set to 0.0427231
[29:12] Peak width: 3.992
[29:12] Scan window radius set to 8
[29:12] Recommended MS1 mass accuracy setting: 14.1496 ppm
[32:51] Optimised mass accuracy: 17.7359 ppm
[50:55] Removing low confidence identifications
[59:32] Precursors at 1% peptidoform FDR: 67757
[59:34] Removing interfering precursors
[59:37] Training neural networks: 144036 targets, 89912 decoys
[59:48] Number of IDs at 0.01 FDR: 104803
[60:02] Precursors at 1% peptidoform FDR: 69014
[60:02] Calculating protein q-values
[60:02] Number of genes identified at 1% FDR: 0 (precursor-level), 0 (protein-level) (inference performed using proteotypic peptides only)
[60:03] Quantification
[60:03] Precursors with monitored PTMs at 1% FDR: 344 out of 37548 considered
[60:03] Unmodified precursors with monitored PTM sites at 1% FDR: 12572
[60:03] Precursors with PTMs localised (when required) with > 90% confidence: 339 out of 344
[60:04] Quantification information saved to D:\Proteobench_manuscript_data\Raw_diaPASEF\Marie_2025\ttSCP_diaPASEF_Condition_A_Sample_Alpha_01_11494.d.quant

[60:05] File #2/6
[60:05] Loading run D:\Proteobench_manuscript_data\Raw_diaPASEF\Marie_2025\ttSCP_diaPASEF_Condition_A_Sample_Alpha_02_11500.d
[61:38] 5116692 library precursors are potentially detectable
[61:40] Processing...
[64:04] RT window set to 1.94613
[64:04] Ion mobility window set to 0.0418814
[64:04] Recommended MS1 mass accuracy setting: 14.7606 ppm
[84:14] Removing low confidence identifications
[93:45] Precursors at 1% peptidoform FDR: 70676
[93:47] Removing interfering precursors
[93:50] Training neural networks: 146706 targets, 91990 decoys
[93:56] Number of IDs at 0.01 FDR: 109127
[94:04] Precursors at 1% peptidoform FDR: 71240
[94:05] Calculating protein q-values
[94:05] Number of genes identified at 1% FDR: 0 (precursor-level), 0 (protein-level) (inference performed using proteotypic peptides only)
[94:05] Quantification
[94:06] Precursors with monitored PTMs at 1% FDR: 513 out of 39808 considered
[94:06] Unmodified precursors with monitored PTM sites at 1% FDR: 12782
[94:06] Precursors with PTMs localised (when required) with > 90% confidence: 503 out of 513
[94:07] Quantification information saved to D:\Proteobench_manuscript_data\Raw_diaPASEF\Marie_2025\ttSCP_diaPASEF_Condition_A_Sample_Alpha_02_11500.d.quant

[94:07] File #3/6
[94:07] Loading run D:\Proteobench_manuscript_data\Raw_diaPASEF\Marie_2025\ttSCP_diaPASEF_Condition_A_Sample_Alpha_03_11506.d
[95:44] 5116692 library precursors are potentially detectable
[95:47] Processing...
[98:08] RT window set to 1.9365
[98:08] Ion mobility window set to 0.0415052
[98:08] Recommended MS1 mass accuracy setting: 13.996 ppm
[118:07] Removing low confidence identifications
[127:36] Precursors at 1% peptidoform FDR: 70735
[127:38] Removing interfering precursors
[127:41] Training neural networks: 151172 targets, 94173 decoys
[127:47] Number of IDs at 0.01 FDR: 109984
[127:56] Precursors at 1% peptidoform FDR: 71126
[127:56] Calculating protein q-values
[127:56] Number of genes identified at 1% FDR: 0 (precursor-level), 0 (protein-level) (inference performed using proteotypic peptides only)
[127:56] Quantification
[127:57] Precursors with monitored PTMs at 1% FDR: 569 out of 39846 considered
[127:57] Unmodified precursors with monitored PTM sites at 1% FDR: 12633
[127:57] Precursors with PTMs localised (when required) with > 90% confidence: 561 out of 569
[128:01] Quantification information saved to D:\Proteobench_manuscript_data\Raw_diaPASEF\Marie_2025\ttSCP_diaPASEF_Condition_A_Sample_Alpha_03_11506.d.quant

[128:01] File #4/6
[128:01] Loading run D:\Proteobench_manuscript_data\Raw_diaPASEF\Marie_2025\ttSCP_diaPASEF_Condition_B_Sample_Alpha_01_11496.d
[129:36] 5116692 library precursors are potentially detectable
[129:39] Processing...
[131:57] RT window set to 1.88935
[131:57] Ion mobility window set to 0.0425337
[131:57] Recommended MS1 mass accuracy setting: 13.3276 ppm
[151:04] Removing low confidence identifications
[160:08] Precursors at 1% peptidoform FDR: 69015
[160:10] Removing interfering precursors
[160:13] Training neural networks: 144507 targets, 90196 decoys
[160:19] Number of IDs at 0.01 FDR: 106755
[160:27] Precursors at 1% peptidoform FDR: 70782
[160:27] Calculating protein q-values
[160:27] Number of genes identified at 1% FDR: 0 (precursor-level), 0 (protein-level) (inference performed using proteotypic peptides only)
[160:27] Quantification
[160:28] Precursors with monitored PTMs at 1% FDR: 630 out of 38342 considered
[160:28] Unmodified precursors with monitored PTM sites at 1% FDR: 12985
[160:28] Precursors with PTMs localised (when required) with > 90% confidence: 620 out of 630
[160:32] Quantification information saved to D:\Proteobench_manuscript_data\Raw_diaPASEF\Marie_2025\ttSCP_diaPASEF_Condition_B_Sample_Alpha_01_11496.d.quant

[160:32] File #5/6
[160:32] Loading run D:\Proteobench_manuscript_data\Raw_diaPASEF\Marie_2025\ttSCP_diaPASEF_Condition_B_Sample_Alpha_02_11502.d
[162:07] 5116692 library precursors are potentially detectable
[162:10] Processing...
[164:53] RT window set to 1.86051
[164:53] Ion mobility window set to 0.0407781
[164:53] Recommended MS1 mass accuracy setting: 14.4153 ppm
[189:47] Removing low confidence identifications
[202:16] Precursors at 1% peptidoform FDR: 69522
[202:19] Removing interfering precursors
[202:26] Training neural networks: 146729 targets, 92229 decoys
[202:35] Number of IDs at 0.01 FDR: 107750
[202:50] Precursors at 1% peptidoform FDR: 71785
[202:51] Calculating protein q-values
[202:51] Number of genes identified at 1% FDR: 0 (precursor-level), 0 (protein-level) (inference performed using proteotypic peptides only)
[202:51] Quantification
[202:53] Precursors with monitored PTMs at 1% FDR: 709 out of 38648 considered
[202:53] Unmodified precursors with monitored PTM sites at 1% FDR: 13196
[202:53] Precursors with PTMs localised (when required) with > 90% confidence: 704 out of 709
[202:58] Quantification information saved to D:\Proteobench_manuscript_data\Raw_diaPASEF\Marie_2025\ttSCP_diaPASEF_Condition_B_Sample_Alpha_02_11502.d.quant

[202:58] File #6/6
[202:58] Loading run D:\Proteobench_manuscript_data\Raw_diaPASEF\Marie_2025\ttSCP_diaPASEF_Condition_B_Sample_Alpha_03_11508.d
[204:37] 5116692 library precursors are potentially detectable
[204:42] Processing...
[207:47] RT window set to 1.93739
[207:47] Ion mobility window set to 0.0418765
[207:47] Recommended MS1 mass accuracy setting: 14.093 ppm
[230:45] Removing low confidence identifications
[243:57] Precursors at 1% peptidoform FDR: 70277
[243:59] Removing interfering precursors
[244:06] Training neural networks: 148423 targets, 93086 decoys
[244:15] Number of IDs at 0.01 FDR: 108347
[244:31] Precursors at 1% peptidoform FDR: 70318
[244:32] Calculating protein q-values
[244:33] Number of genes identified at 1% FDR: 0 (precursor-level), 0 (protein-level) (inference performed using proteotypic peptides only)
[244:33] Quantification
[244:34] Precursors with monitored PTMs at 1% FDR: 647 out of 38270 considered
[244:34] Unmodified precursors with monitored PTM sites at 1% FDR: 12835
[244:34] Precursors with PTMs localised (when required) with > 90% confidence: 637 out of 647
[244:36] Quantification information saved to D:\Proteobench_manuscript_data\Raw_diaPASEF\Marie_2025\ttSCP_diaPASEF_Condition_B_Sample_Alpha_03_11508.d.quant

[244:36] Cross-run analysis
[244:36] Reading quantification information: 6 files
[244:49] Quantifying peptides
[245:04] Assembling protein groups
[245:07] Quantifying proteins
[245:08] Calculating q-values for protein and gene groups
[245:09] Calculating global q-values for protein and gene groups
[245:10] Protein groups with global q-value <= 0.01: 26423
[245:14] Compressed report saved to D:\Proteobench_manuscript_data\run_output_diaPASEF\diann_1.9.1_default\report-first-pass.parquet. Use R 'arrow' or Python 'PyArrow' package to process
[245:14] Writing report
[245:30] Report saved to D:\Proteobench_manuscript_data\run_output_diaPASEF\diann_1.9.1_default\report-first-pass.tsv.
[245:30] Saving precursor levels matrix
[245:31] Precursor levels matrix (1% precursor and protein group FDR) saved to D:\Proteobench_manuscript_data\run_output_diaPASEF\diann_1.9.1_default\report-first-pass.pr_matrix.tsv.
[245:31] Manifest saved to D:\Proteobench_manuscript_data\run_output_diaPASEF\diann_1.9.1_default\report-first-pass.manifest.txt
[245:31] Stats report saved to D:\Proteobench_manuscript_data\run_output_diaPASEF\diann_1.9.1_default\report-first-pass.stats.tsv
[245:31] Generating spectral library:
[245:33] 192628 target and 1794 decoy precursors saved
[245:33] Spectral library saved to D:\Proteobench_manuscript_data\run_output_diaPASEF\diann_1.9.1_default\report-lib.parquet

[245:34] Loading spectral library D:\Proteobench_manuscript_data\run_output_diaPASEF\diann_1.9.1_default\report-lib.parquet
[245:35] Spectral library loaded: 27083 protein isoforms, 26716 protein groups and 194422 precursors in 186986 elution groups.
[245:35] Loading protein annotations from FASTA D:\Proteobench_manuscript_data\ProteoBenchFASTA_DDAQuantification.fasta
[245:36] Annotating library proteins with information from the FASTA database
[245:36] Protein names missing for some isoforms
[245:36] Gene names missing for some isoforms
[245:36] Library contains 27000 proteins, and 0 genes
[245:36] Initialising library
[245:36] Saving the library to D:\Proteobench_manuscript_data\run_output_diaPASEF\diann_1.9.1_default\report-lib.parquet.skyline.speclib


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

[245:36] File #1/6
[245:36] Loading run D:\Proteobench_manuscript_data\Raw_diaPASEF\Marie_2025\ttSCP_diaPASEF_Condition_A_Sample_Alpha_01_11494.d
[247:17] 192628 library precursors are potentially detectable
[247:17] Processing...
[247:26] RT window set to 0.611427
[247:26] Ion mobility window set to 0.01
[247:26] Recommended MS1 mass accuracy setting: 13.2044 ppm
[247:47] Removing low confidence identifications
[247:58] Precursors at 1% peptidoform FDR: 69037
[247:59] Removing interfering precursors
[248:00] Training neural networks: 150555 targets, 82074 decoys
[248:05] Number of IDs at 0.01 FDR: 104428
[248:11] Precursors at 1% peptidoform FDR: 77046
[248:11] Calculating protein q-values
[248:11] Number of genes identified at 1% FDR: 0 (precursor-level), 0 (protein-level) (inference performed using proteotypic peptides only)
[248:11] Quantification
[248:11] Precursors with monitored PTMs at 1% FDR: 650 out of 30708 considered
[248:11] Unmodified precursors with monitored PTM sites at 1% FDR: 14621
[248:11] Precursors with PTMs localised (when required) with > 90% confidence: 644 out of 650

[248:12] File #2/6
[248:12] Loading run D:\Proteobench_manuscript_data\Raw_diaPASEF\Marie_2025\ttSCP_diaPASEF_Condition_A_Sample_Alpha_02_11500.d
[249:42] 192628 library precursors are potentially detectable
[249:42] Processing...
[249:48] RT window set to 0.615685
[249:48] Ion mobility window set to 0.01
[249:48] Recommended MS1 mass accuracy setting: 13.8728 ppm
[250:10] Removing low confidence identifications
[250:20] Precursors at 1% peptidoform FDR: 70530
[250:22] Removing interfering precursors
[250:23] Training neural networks: 151880 targets, 82555 decoys
[250:28] Number of IDs at 0.01 FDR: 106333
[250:34] Precursors at 1% peptidoform FDR: 78214
[250:34] Calculating protein q-values
[250:34] Number of genes identified at 1% FDR: 0 (precursor-level), 0 (protein-level) (inference performed using proteotypic peptides only)
[250:34] Quantification
[250:34] Precursors with monitored PTMs at 1% FDR: 671 out of 31299 considered
[250:34] Unmodified precursors with monitored PTM sites at 1% FDR: 14850
[250:34] Precursors with PTMs localised (when required) with > 90% confidence: 663 out of 671

[250:35] File #3/6
[250:35] Loading run D:\Proteobench_manuscript_data\Raw_diaPASEF\Marie_2025\ttSCP_diaPASEF_Condition_A_Sample_Alpha_03_11506.d
[252:08] 192628 library precursors are potentially detectable
[252:08] Processing...
[252:15] RT window set to 0.615949
[252:15] Ion mobility window set to 0.01
[252:15] Recommended MS1 mass accuracy setting: 14.8574 ppm
[252:37] Removing low confidence identifications
[252:48] Precursors at 1% peptidoform FDR: 70598
[252:50] Removing interfering precursors
[252:51] Training neural networks: 151461 targets, 82488 decoys
[252:56] Number of IDs at 0.01 FDR: 105865
[253:02] Precursors at 1% peptidoform FDR: 77449
[253:02] Calculating protein q-values
[253:02] Number of genes identified at 1% FDR: 0 (precursor-level), 0 (protein-level) (inference performed using proteotypic peptides only)
[253:02] Quantification
[253:02] Precursors with monitored PTMs at 1% FDR: 657 out of 31836 considered
[253:02] Unmodified precursors with monitored PTM sites at 1% FDR: 14632
[253:02] Precursors with PTMs localised (when required) with > 90% confidence: 652 out of 657

[253:03] File #4/6
[253:03] Loading run D:\Proteobench_manuscript_data\Raw_diaPASEF\Marie_2025\ttSCP_diaPASEF_Condition_B_Sample_Alpha_01_11496.d
[254:36] 192628 library precursors are potentially detectable
[254:36] Processing...
[254:42] RT window set to 0.61291
[254:42] Ion mobility window set to 0.01
[254:42] Recommended MS1 mass accuracy setting: 14.3781 ppm
[255:03] Removing low confidence identifications
[255:14] Precursors at 1% peptidoform FDR: 66757
[255:15] Removing interfering precursors
[255:16] Training neural networks: 149164 targets, 81127 decoys
[255:21] Number of IDs at 0.01 FDR: 104866
[255:27] Precursors at 1% peptidoform FDR: 77725
[255:27] Calculating protein q-values
[255:27] Number of genes identified at 1% FDR: 0 (precursor-level), 0 (protein-level) (inference performed using proteotypic peptides only)
[255:27] Quantification
[255:27] Precursors with monitored PTMs at 1% FDR: 634 out of 30778 considered
[255:27] Unmodified precursors with monitored PTM sites at 1% FDR: 14764
[255:27] Precursors with PTMs localised (when required) with > 90% confidence: 628 out of 634

[255:28] File #5/6
[255:28] Loading run D:\Proteobench_manuscript_data\Raw_diaPASEF\Marie_2025\ttSCP_diaPASEF_Condition_B_Sample_Alpha_02_11502.d
[257:00] 192628 library precursors are potentially detectable
[257:00] Processing...
[257:07] RT window set to 0.618132
[257:07] Ion mobility window set to 0.01
[257:07] Recommended MS1 mass accuracy setting: 13.8294 ppm
[257:28] Removing low confidence identifications
[257:38] Precursors at 1% peptidoform FDR: 68858
[257:40] Removing interfering precursors
[257:41] Training neural networks: 150364 targets, 81979 decoys
[257:46] Number of IDs at 0.01 FDR: 106710
[257:51] Precursors at 1% peptidoform FDR: 78174
[257:51] Calculating protein q-values
[257:51] Number of genes identified at 1% FDR: 0 (precursor-level), 0 (protein-level) (inference performed using proteotypic peptides only)
[257:51] Quantification
[257:52] Precursors with monitored PTMs at 1% FDR: 679 out of 32272 considered
[257:52] Unmodified precursors with monitored PTM sites at 1% FDR: 14876
[257:52] Precursors with PTMs localised (when required) with > 90% confidence: 673 out of 679

[257:53] File #6/6
[257:53] Loading run D:\Proteobench_manuscript_data\Raw_diaPASEF\Marie_2025\ttSCP_diaPASEF_Condition_B_Sample_Alpha_03_11508.d
[259:30] 192628 library precursors are potentially detectable
[259:31] Processing...
[259:37] RT window set to 0.61491
[259:37] Ion mobility window set to 0.01
[259:37] Recommended MS1 mass accuracy setting: 14.9179 ppm
[259:59] Removing low confidence identifications
[260:10] Precursors at 1% peptidoform FDR: 70334
[260:12] Removing interfering precursors
[260:13] Training neural networks: 151006 targets, 82401 decoys
[260:19] Number of IDs at 0.01 FDR: 105932
[260:25] Precursors at 1% peptidoform FDR: 78020
[260:25] Calculating protein q-values
[260:25] Number of genes identified at 1% FDR: 0 (precursor-level), 0 (protein-level) (inference performed using proteotypic peptides only)
[260:25] Quantification
[260:25] Precursors with monitored PTMs at 1% FDR: 677 out of 30970 considered
[260:25] Unmodified precursors with monitored PTM sites at 1% FDR: 14823
[260:25] Precursors with PTMs localised (when required) with > 90% confidence: 671 out of 677

[260:26] Cross-run analysis
[260:26] Reading quantification information: 6 files
[260:28] Quantifying peptides
[262:07] Quantification parameters: 0.326858, 0.00150735, 0.00397129, 0.0278806, 0.0590085, 0.0488744, 0.258007, 0.0136837, 0.0418425, 0.0525452, 0.0757625, 0.0698241, 0.185408, 0.0807012, 0.0892463, 0.0123569
[262:12] Quantifying proteins
[262:12] Calculating q-values for protein and gene groups
[262:12] Calculating global q-values for protein and gene groups
[262:12] Protein groups with global q-value <= 0.01: 19053
[262:15] Compressed report saved to D:\Proteobench_manuscript_data\run_output_diaPASEF\diann_1.9.1_default\report.parquet. Use R 'arrow' or Python 'PyArrow' package to process
[262:15] Writing report
[262:31] Report saved to D:\Proteobench_manuscript_data\run_output_diaPASEF\diann_1.9.1_default\report.tsv.
[262:31] Saving precursor levels matrix
[262:31] Precursor levels matrix (1% precursor and protein group FDR) saved to D:\Proteobench_manuscript_data\run_output_diaPASEF\diann_1.9.1_default\report.pr_matrix.tsv.
[262:31] Saving protein group levels matrix
[262:31] Protein group levels matrix (1% precursor FDR and protein group FDR) saved to D:\Proteobench_manuscript_data\run_output_diaPASEF\diann_1.9.1_default\report.pg_matrix.tsv.
[262:31] Saving gene group levels matrix
[262:31] Gene groups levels matrix (1% precursor FDR and protein group FDR) saved to D:\Proteobench_manuscript_data\run_output_diaPASEF\diann_1.9.1_default\report.gg_matrix.tsv.
[262:31] Saving unique genes levels matrix
[262:31] Unique genes levels matrix (1% precursor FDR and protein group FDR) saved to D:\Proteobench_manuscript_data\run_output_diaPASEF\diann_1.9.1_default\report.unique_genes_matrix.tsv.
[262:31] Manifest saved to D:\Proteobench_manuscript_data\run_output_diaPASEF\diann_1.9.1_default\report.manifest.txt
[262:31] Stats report saved to D:\Proteobench_manuscript_data\run_output_diaPASEF\diann_1.9.1_default\report.stats.tsv

