DIA-NN 1.9.2 (Data-Independent Acquisition by Neural Networks)
Compiled on Oct 17 2024 21:58:43
Current date and time: Thu Oct 31 19:11:44 2024
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\LFQ_Orbitrap_AIF_Condition_A_Sample_Alpha_01.mzML  --f D:\Proteobench_manuscript_data\LFQ_Orbitrap_AIF_Condition_A_Sample_Alpha_02.mzML  --f D:\Proteobench_manuscript_data\LFQ_Orbitrap_AIF_Condition_A_Sample_Alpha_03.mzML  --f D:\Proteobench_manuscript_data\LFQ_Orbitrap_AIF_Condition_B_Sample_Alpha_01.mzML  --f D:\Proteobench_manuscript_data\LFQ_Orbitrap_AIF_Condition_B_Sample_Alpha_02.mzML  --f D:\Proteobench_manuscript_data\LFQ_Orbitrap_AIF_Condition_B_Sample_Alpha_03.mzML  --lib  --threads 20 --verbose 1 --out D:\Proteobench_manuscript_data\run_output\diann_1.9.2_legacy_quant\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 --direct-quant 

Thread number set to 20
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
Legacy (direct) quantification mode
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] [0:21] [11:20] [12:53] [12:57] [12:57] Saving the library to C:\DIA-NN\1.9.2\report-lib.predicted.speclib
[13:07] Initialising library
[13:17] Loading spectral library C:\DIA-NN\1.9.2\report-lib.predicted.speclib
[13:20] Library annotated with sequence database(s): D:\Proteobench_manuscript_data\ProteoBenchFASTA_DDAQuantification.fasta
[13:21] Spectral library loaded: 31837 protein isoforms, 51765 protein groups and 5116692 precursors in 2716663 elution groups.
[13:21] Loading protein annotations from FASTA D:\Proteobench_manuscript_data\ProteoBenchFASTA_DDAQuantification.fasta
[13:21] Annotating library proteins with information from the FASTA database
[13:21] Protein names missing for some isoforms
[13:21] Gene names missing for some isoforms
[13:21] Library contains 31685 proteins, and 0 genes
[13:25] Initialising library

First pass: generating a spectral library from DIA data

[13:35] File #1/6
[13:35] Loading run D:\Proteobench_manuscript_data\LFQ_Orbitrap_AIF_Condition_A_Sample_Alpha_01.mzML
[14:13] 5116692 library precursors are potentially detectable
[14:16] Calibrating with mass accuracies 30 (MS1), 20 (MS2)
[14:56] RT window set to 6.99348
[14:56] Peak width: 6.164
[14:56] Scan window radius set to 13
[14:56] Recommended MS1 mass accuracy setting: 9.32611 ppm
[16:27] Optimised mass accuracy: 14.3505 ppm
[20:06] Removing low confidence identifications
[21:42] Precursors at 1% peptidoform FDR: 56257
[21:43] Removing interfering precursors
[21:47] Training neural networks on 205758 PSMs
[21:53] Number of IDs at 0.01 FDR: 79638
[21:56] Precursors at 1% peptidoform FDR: 68215
[21:56] Calculating protein q-values
[21:56] Number of genes identified at 1% FDR: 0 (precursor-level), 0 (protein-level) (inference performed using proteotypic peptides only)
[21:56] Quantification
[21:57] Precursors with monitored PTMs at 1% FDR: 4560 out of 15360 considered
[21:57] Unmodified precursors with monitored PTM sites at 1% FDR: 8204
[21:57] Precursors with PTMs localised (when required) with > 90% confidence: 4486 out of 4560
[22:01] Quantification information saved to D:\Proteobench_manuscript_data\LFQ_Orbitrap_AIF_Condition_A_Sample_Alpha_01.mzML.quant

[22:01] File #2/6
[22:01] Loading run D:\Proteobench_manuscript_data\LFQ_Orbitrap_AIF_Condition_A_Sample_Alpha_02.mzML
[22:36] 5116692 library precursors are potentially detectable
[22:39] Calibrating with mass accuracies 30 (MS1), 14.3505 (MS2)
[23:12] RT window set to 6.53529
[23:12] Recommended MS1 mass accuracy setting: 8.70693 ppm
[26:41] Removing low confidence identifications
[28:25] Precursors at 1% peptidoform FDR: 58140
[28:27] Removing interfering precursors
[28:31] Training neural networks on 219368 PSMs
[28:38] Number of IDs at 0.01 FDR: 81221
[28:41] Precursors at 1% peptidoform FDR: 69221
[28:42] Calculating protein q-values
[28:42] Number of genes identified at 1% FDR: 0 (precursor-level), 0 (protein-level) (inference performed using proteotypic peptides only)
[28:42] Quantification
[28:43] Precursors with monitored PTMs at 1% FDR: 4286 out of 16428 considered
[28:43] Unmodified precursors with monitored PTM sites at 1% FDR: 9442
[28:43] Precursors with PTMs localised (when required) with > 90% confidence: 4202 out of 4286
[28:43] Quantification information saved to D:\Proteobench_manuscript_data\LFQ_Orbitrap_AIF_Condition_A_Sample_Alpha_02.mzML.quant

[28:43] File #3/6
[28:43] Loading run D:\Proteobench_manuscript_data\LFQ_Orbitrap_AIF_Condition_A_Sample_Alpha_03.mzML
[29:27] 5116692 library precursors are potentially detectable
[29:31] Calibrating with mass accuracies 30 (MS1), 14.3505 (MS2)
[30:13] RT window set to 6.89902
[30:13] Recommended MS1 mass accuracy setting: 8.62496 ppm
[33:49] Removing low confidence identifications
[35:32] Precursors at 1% peptidoform FDR: 52260
[35:33] Removing interfering precursors
[35:38] Training neural networks on 195394 PSMs
[35:45] Number of IDs at 0.01 FDR: 74519
[35:48] Precursors at 1% peptidoform FDR: 63484
[35:48] Calculating protein q-values
[35:48] Number of genes identified at 1% FDR: 0 (precursor-level), 0 (protein-level) (inference performed using proteotypic peptides only)
[35:48] Quantification
[35:49] Precursors with monitored PTMs at 1% FDR: 3721 out of 14723 considered
[35:49] Unmodified precursors with monitored PTM sites at 1% FDR: 8290
[35:49] Precursors with PTMs localised (when required) with > 90% confidence: 3652 out of 3721
[35:50] Quantification information saved to D:\Proteobench_manuscript_data\LFQ_Orbitrap_AIF_Condition_A_Sample_Alpha_03.mzML.quant

[35:50] File #4/6
[35:50] Loading run D:\Proteobench_manuscript_data\LFQ_Orbitrap_AIF_Condition_B_Sample_Alpha_01.mzML
[36:33] 5116692 library precursors are potentially detectable
[36:38] Calibrating with mass accuracies 30 (MS1), 14.3505 (MS2)
[37:20] RT window set to 7.85455
[37:20] Recommended MS1 mass accuracy setting: 8.31828 ppm
[41:23] Removing low confidence identifications
[43:18] Precursors at 1% peptidoform FDR: 49933
[43:19] Removing interfering precursors
[43:23] Training neural networks on 185313 PSMs
[43:29] Number of IDs at 0.01 FDR: 70569
[43:32] Precursors at 1% peptidoform FDR: 61274
[43:33] Calculating protein q-values
[43:33] Number of genes identified at 1% FDR: 0 (precursor-level), 0 (protein-level) (inference performed using proteotypic peptides only)
[43:33] Quantification
[43:33] Precursors with monitored PTMs at 1% FDR: 9693 out of 11927 considered
[43:33] Unmodified precursors with monitored PTM sites at 1% FDR: 483
[43:34] Precursors with PTMs localised (when required) with > 90% confidence: 9690 out of 9693
[43:34] Quantification information saved to D:\Proteobench_manuscript_data\LFQ_Orbitrap_AIF_Condition_B_Sample_Alpha_01.mzML.quant

[43:34] File #5/6
[43:34] Loading run D:\Proteobench_manuscript_data\LFQ_Orbitrap_AIF_Condition_B_Sample_Alpha_02.mzML
[44:21] 5116692 library precursors are potentially detectable
[44:26] Calibrating with mass accuracies 30 (MS1), 14.3505 (MS2)
[45:10] RT window set to 7.68413
[45:10] Recommended MS1 mass accuracy setting: 8.88762 ppm
[49:10] Removing low confidence identifications
[51:00] Precursors at 1% peptidoform FDR: 52112
[51:01] Removing interfering precursors
[51:04] Training neural networks on 199998 PSMs
[51:10] Number of IDs at 0.01 FDR: 72785
[51:13] Precursors at 1% peptidoform FDR: 62110
[51:13] Calculating protein q-values
[51:13] Number of genes identified at 1% FDR: 0 (precursor-level), 0 (protein-level) (inference performed using proteotypic peptides only)
[51:13] Quantification
[51:14] Precursors with monitored PTMs at 1% FDR: 9504 out of 12350 considered
[51:14] Unmodified precursors with monitored PTM sites at 1% FDR: 667
[51:14] Precursors with PTMs localised (when required) with > 90% confidence: 9498 out of 9504
[51:14] Quantification information saved to D:\Proteobench_manuscript_data\LFQ_Orbitrap_AIF_Condition_B_Sample_Alpha_02.mzML.quant

[51:14] File #6/6
[51:14] Loading run D:\Proteobench_manuscript_data\LFQ_Orbitrap_AIF_Condition_B_Sample_Alpha_03.mzML
[51:56] 5116692 library precursors are potentially detectable
[51:59] Calibrating with mass accuracies 30 (MS1), 14.3505 (MS2)
[52:37] RT window set to 8.48596
[52:37] Recommended MS1 mass accuracy setting: 9.06837 ppm
[56:08] Removing low confidence identifications
[57:48] Precursors at 1% peptidoform FDR: 45567
[57:49] Removing interfering precursors
[57:53] Training neural networks on 177434 PSMs
[57:58] Number of IDs at 0.01 FDR: 67144
[58:00] Precursors at 1% peptidoform FDR: 57347
[58:00] Calculating protein q-values
[58:01] Number of genes identified at 1% FDR: 0 (precursor-level), 0 (protein-level) (inference performed using proteotypic peptides only)
[58:01] Quantification
[58:01] Precursors with monitored PTMs at 1% FDR: 8781 out of 11350 considered
[58:01] Unmodified precursors with monitored PTM sites at 1% FDR: 760
[58:01] Precursors with PTMs localised (when required) with > 90% confidence: 8776 out of 8781
[58:01] Quantification information saved to D:\Proteobench_manuscript_data\LFQ_Orbitrap_AIF_Condition_B_Sample_Alpha_03.mzML.quant

[58:01] Cross-run analysis
[58:01] Reading quantification information: 6 files
[58:05] Quantifying peptides
[58:23] Assembling protein groups
[58:25] Quantifying proteins
[58:25] Calculating q-values for protein and gene groups
[58:26] Calculating global q-values for protein and gene groups
[58:26] Protein groups with global q-value <= 0.01: 9190
[58:27] Compressed report saved to D:\Proteobench_manuscript_data\run_output\diann_1.9.2_legacy_quant\report-first-pass.parquet. Use R 'arrow' or Python 'PyArrow' package to process
[58:27] Writing report
[58:38] Report saved to D:\Proteobench_manuscript_data\run_output\diann_1.9.2_legacy_quant\report-first-pass.tsv.
[58:38] Saving precursor levels matrix
[58:38] Precursor levels matrix (1% precursor and protein group FDR) saved to D:\Proteobench_manuscript_data\run_output\diann_1.9.2_legacy_quant\report-first-pass.pr_matrix.tsv.
[58:39] Manifest saved to D:\Proteobench_manuscript_data\run_output\diann_1.9.2_legacy_quant\report-first-pass.manifest.txt
[58:39] Stats report saved to D:\Proteobench_manuscript_data\run_output\diann_1.9.2_legacy_quant\report-first-pass.stats.tsv
[58:39] Generating spectral library:
[58:40] 98174 target and 1003 decoy precursors saved
[58:40] Spectral library saved to C:\DIA-NN\1.9.2\report-lib.parquet

[58:40] Loading spectral library C:\DIA-NN\1.9.2\report-lib.parquet
[58:41] Spectral library loaded: 10955 protein isoforms, 10776 protein groups and 99177 precursors in 89533 elution groups.
[58:41] Loading protein annotations from FASTA D:\Proteobench_manuscript_data\ProteoBenchFASTA_DDAQuantification.fasta
[58:41] Annotating library proteins with information from the FASTA database
[58:41] Protein names missing for some isoforms
[58:41] Gene names missing for some isoforms
[58:41] Library contains 10943 proteins, and 0 genes
[58:41] Initialising library
[58:42] 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

[58:42] File #1/6
[58:42] Loading run D:\Proteobench_manuscript_data\LFQ_Orbitrap_AIF_Condition_A_Sample_Alpha_01.mzML
[59:23] 98174 library precursors are potentially detectable
[59:23] Calibrating with mass accuracies 30 (MS1), 14.3505 (MS2)
[59:24] RT window set to 2.65894
[59:24] Recommended MS1 mass accuracy setting: 9.12026 ppm
[59:28] Removing low confidence identifications
[59:32] Precursors at 1% peptidoform FDR: 66526
[59:32] Removing interfering precursors
[59:33] Training neural networks on 120711 PSMs
[59:36] Number of IDs at 0.01 FDR: 84418
[59:39] Precursors at 1% peptidoform FDR: 74636
[59:39] Calculating protein q-values
[59:39] Number of genes identified at 1% FDR: 0 (precursor-level), 0 (protein-level) (inference performed using proteotypic peptides only)
[59:39] Quantification
[59:39] Precursors with monitored PTMs at 1% FDR: 6799 out of 17080 considered
[59:39] Unmodified precursors with monitored PTM sites at 1% FDR: 8697
[59:39] Precursors with PTMs localised (when required) with > 90% confidence: 6735 out of 6799

[59:39] File #2/6
[59:39] Loading run D:\Proteobench_manuscript_data\LFQ_Orbitrap_AIF_Condition_A_Sample_Alpha_02.mzML
[60:16] 98174 library precursors are potentially detectable
[60:16] Calibrating with mass accuracies 30 (MS1), 14.3505 (MS2)
[60:16] RT window set to 2.56179
[60:16] Recommended MS1 mass accuracy setting: 8.98468 ppm
[60:21] Removing low confidence identifications
[60:24] Precursors at 1% peptidoform FDR: 68147
[60:25] Removing interfering precursors
[60:25] Training neural networks on 121158 PSMs
[60:28] Number of IDs at 0.01 FDR: 85636
[60:31] Precursors at 1% peptidoform FDR: 75177
[60:31] Calculating protein q-values
[60:31] Number of genes identified at 1% FDR: 0 (precursor-level), 0 (protein-level) (inference performed using proteotypic peptides only)
[60:31] Quantification
[60:31] Precursors with monitored PTMs at 1% FDR: 6763 out of 17716 considered
[60:31] Unmodified precursors with monitored PTM sites at 1% FDR: 8945
[60:31] Precursors with PTMs localised (when required) with > 90% confidence: 6709 out of 6763

[60:31] File #3/6
[60:31] Loading run D:\Proteobench_manuscript_data\LFQ_Orbitrap_AIF_Condition_A_Sample_Alpha_03.mzML
[61:07] 98174 library precursors are potentially detectable
[61:07] Calibrating with mass accuracies 30 (MS1), 14.3505 (MS2)
[61:08] RT window set to 2.6365
[61:08] Recommended MS1 mass accuracy setting: 9.31682 ppm
[61:12] Removing low confidence identifications
[61:15] Precursors at 1% peptidoform FDR: 62848
[61:15] Removing interfering precursors
[61:16] Training neural networks on 118227 PSMs
[61:19] Number of IDs at 0.01 FDR: 81457
[61:21] Precursors at 1% peptidoform FDR: 72404
[61:21] Calculating protein q-values
[61:21] Number of genes identified at 1% FDR: 0 (precursor-level), 0 (protein-level) (inference performed using proteotypic peptides only)
[61:21] Quantification
[61:22] Precursors with monitored PTMs at 1% FDR: 6251 out of 16555 considered
[61:22] Unmodified precursors with monitored PTM sites at 1% FDR: 8747
[61:22] Precursors with PTMs localised (when required) with > 90% confidence: 6194 out of 6251

[61:22] File #4/6
[61:22] Loading run D:\Proteobench_manuscript_data\LFQ_Orbitrap_AIF_Condition_B_Sample_Alpha_01.mzML
[61:57] 98174 library precursors are potentially detectable
[61:57] Calibrating with mass accuracies 30 (MS1), 14.3505 (MS2)
[61:58] RT window set to 2.64445
[61:58] Recommended MS1 mass accuracy setting: 8.53644 ppm
[62:02] Removing low confidence identifications
[62:05] Precursors at 1% peptidoform FDR: 59105
[62:06] Removing interfering precursors
[62:07] Training neural networks on 114180 PSMs
[62:10] Number of IDs at 0.01 FDR: 73552
[62:12] Precursors at 1% peptidoform FDR: 64851
[62:12] Calculating protein q-values
[62:12] Number of genes identified at 1% FDR: 0 (precursor-level), 0 (protein-level) (inference performed using proteotypic peptides only)
[62:12] Quantification
[62:12] Precursors with monitored PTMs at 1% FDR: 9364 out of 11352 considered
[62:12] Unmodified precursors with monitored PTM sites at 1% FDR: 773
[62:12] Precursors with PTMs localised (when required) with > 90% confidence: 9355 out of 9364

[62:12] File #5/6
[62:12] Loading run D:\Proteobench_manuscript_data\LFQ_Orbitrap_AIF_Condition_B_Sample_Alpha_02.mzML
[62:48] 98174 library precursors are potentially detectable
[62:48] Calibrating with mass accuracies 30 (MS1), 14.3505 (MS2)
[62:49] RT window set to 2.55149
[62:49] Recommended MS1 mass accuracy setting: 9.25492 ppm
[62:53] Removing low confidence identifications
[62:57] Precursors at 1% peptidoform FDR: 62092
[62:57] Removing interfering precursors
[62:58] Training neural networks on 115592 PSMs
[63:01] Number of IDs at 0.01 FDR: 75647
[63:03] Precursors at 1% peptidoform FDR: 67191
[63:03] Calculating protein q-values
[63:03] Number of genes identified at 1% FDR: 0 (precursor-level), 0 (protein-level) (inference performed using proteotypic peptides only)
[63:03] Quantification
[63:03] Precursors with monitored PTMs at 1% FDR: 9582 out of 12395 considered
[63:03] Unmodified precursors with monitored PTM sites at 1% FDR: 1538
[63:03] Precursors with PTMs localised (when required) with > 90% confidence: 9556 out of 9582

[63:04] File #6/6
[63:04] Loading run D:\Proteobench_manuscript_data\LFQ_Orbitrap_AIF_Condition_B_Sample_Alpha_03.mzML
[63:39] 98174 library precursors are potentially detectable
[63:39] Calibrating with mass accuracies 30 (MS1), 14.3505 (MS2)
[63:40] RT window set to 2.65916
[63:40] Recommended MS1 mass accuracy setting: 8.85952 ppm
[63:44] Removing low confidence identifications
[63:47] Precursors at 1% peptidoform FDR: 55998
[63:47] Removing interfering precursors
[63:48] Training neural networks on 112090 PSMs
[63:51] Number of IDs at 0.01 FDR: 71317
[63:53] Precursors at 1% peptidoform FDR: 63989
[63:53] Calculating protein q-values
[63:53] Number of genes identified at 1% FDR: 0 (precursor-level), 0 (protein-level) (inference performed using proteotypic peptides only)
[63:53] Quantification
[63:53] Precursors with monitored PTMs at 1% FDR: 9250 out of 11587 considered
[63:53] Unmodified precursors with monitored PTM sites at 1% FDR: 1192
[63:53] Precursors with PTMs localised (when required) with > 90% confidence: 9222 out of 9250

[63:53] Cross-run analysis
[63:53] Reading quantification information: 6 files
[63:54] Quantifying peptides
[64:09] Quantifying proteins
[64:09] Calculating q-values for protein and gene groups
[64:09] Calculating global q-values for protein and gene groups
[64:09] Protein groups with global q-value <= 0.01: 9160
[64:11] Compressed report saved to D:\Proteobench_manuscript_data\run_output\diann_1.9.2_legacy_quant\report.parquet. Use R 'arrow' or Python 'PyArrow' package to process
[64:11] Writing report
[64:22] Report saved to D:\Proteobench_manuscript_data\run_output\diann_1.9.2_legacy_quant\report.tsv.
[64:22] Saving precursor levels matrix
[64:22] Precursor levels matrix (1% precursor and protein group FDR) saved to D:\Proteobench_manuscript_data\run_output\diann_1.9.2_legacy_quant\report.pr_matrix.tsv.
[64:22] Saving protein group levels matrix
[64:22] Protein group levels matrix (1% precursor FDR and protein group FDR) saved to D:\Proteobench_manuscript_data\run_output\diann_1.9.2_legacy_quant\report.pg_matrix.tsv.
[64:22] Saving gene group levels matrix
[64:22] Gene groups levels matrix (1% precursor FDR and protein group FDR) saved to D:\Proteobench_manuscript_data\run_output\diann_1.9.2_legacy_quant\report.gg_matrix.tsv.
[64:22] Saving unique genes levels matrix
[64:22] Unique genes levels matrix (1% precursor FDR and protein group FDR) saved to D:\Proteobench_manuscript_data\run_output\diann_1.9.2_legacy_quant\report.unique_genes_matrix.tsv.
[64:22] Manifest saved to D:\Proteobench_manuscript_data\run_output\diann_1.9.2_legacy_quant\report.manifest.txt
[64:22] Stats report saved to D:\Proteobench_manuscript_data\run_output\diann_1.9.2_legacy_quant\report.stats.tsv

