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 21:56:48 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 24 --verbose 1 --out D:\Proteobench_manuscript_data\run_output\diann_1.9.2_conservativenn\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 --conservative --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
Conservative machine learning mode enabled
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
[0:16] [0:20] [9:28] [11:02] [11:06] [11:07] Saving the library to C:\DIA-NN\1.9.2\report-lib.predicted.speclib
[11:20] Initialising library
[11:30] Loading spectral library C:\DIA-NN\1.9.2\report-lib.predicted.speclib
[11:35] Library annotated with sequence database(s): D:\Proteobench_manuscript_data\ProteoBenchFASTA_DDAQuantification.fasta
[11:36] Spectral library loaded: 31837 protein isoforms, 51765 protein groups and 5116692 precursors in 2716663 elution groups.
[11:36] Loading protein annotations from FASTA D:\Proteobench_manuscript_data\ProteoBenchFASTA_DDAQuantification.fasta
[11:36] Annotating library proteins with information from the FASTA database
[11:37] Protein names missing for some isoforms
[11:37] Gene names missing for some isoforms
[11:37] Library contains 31685 proteins, and 0 genes
[11:43] Initialising library

First pass: generating a spectral library from DIA data

[11:56] File #1/6
[11:56] Loading run D:\Proteobench_manuscript_data\LFQ_Orbitrap_AIF_Condition_A_Sample_Alpha_01.mzML
[12:44] 5116692 library precursors are potentially detectable
[12:48] Calibrating with mass accuracies 30 (MS1), 20 (MS2)
[13:20] RT window set to 7.2141
[13:20] Peak width: 6.196
[13:20] Scan window radius set to 13
[13:20] Recommended MS1 mass accuracy setting: 9.02675 ppm
[14:45] Optimised mass accuracy: 14.0714 ppm
[18:48] Removing low confidence identifications
[20:29] Precursors at 1% peptidoform FDR: 57541
[20:31] Removing interfering precursors
[20:35] Training neural networks on 206545 PSMs
[20:41] Number of IDs at 0.01 FDR: 77383
[20:44] Precursors at 1% peptidoform FDR: 72507
[20:44] Calculating protein q-values
[20:45] Number of genes identified at 1% FDR: 0 (precursor-level), 0 (protein-level) (inference performed using proteotypic peptides only)
[20:45] Quantification
[20:45] Precursors with monitored PTMs at 1% FDR: 4743 out of 15279 considered
[20:45] Unmodified precursors with monitored PTM sites at 1% FDR: 8685
[20:45] Precursors with PTMs localised (when required) with > 90% confidence: 4663 out of 4743
[20:49] Quantification information saved to D:\Proteobench_manuscript_data\LFQ_Orbitrap_AIF_Condition_A_Sample_Alpha_01.mzML.quant

[20:49] File #2/6
[20:49] Loading run D:\Proteobench_manuscript_data\LFQ_Orbitrap_AIF_Condition_A_Sample_Alpha_02.mzML
[21:36] 5116692 library precursors are potentially detectable
[21:40] Calibrating with mass accuracies 30 (MS1), 14.0714 (MS2)
[22:11] RT window set to 7.10026
[22:11] Recommended MS1 mass accuracy setting: 8.60986 ppm
[25:50] Removing low confidence identifications
[27:34] Precursors at 1% peptidoform FDR: 58944
[27:35] Removing interfering precursors
[27:39] Training neural networks on 218806 PSMs
[27:46] Number of IDs at 0.01 FDR: 79643
[27:49] Precursors at 1% peptidoform FDR: 74571
[27:50] Calculating protein q-values
[27:50] Number of genes identified at 1% FDR: 0 (precursor-level), 0 (protein-level) (inference performed using proteotypic peptides only)
[27:50] Quantification
[27:51] Precursors with monitored PTMs at 1% FDR: 4721 out of 16350 considered
[27:51] Unmodified precursors with monitored PTM sites at 1% FDR: 10046
[27:51] Precursors with PTMs localised (when required) with > 90% confidence: 4636 out of 4721
[27:51] Quantification information saved to D:\Proteobench_manuscript_data\LFQ_Orbitrap_AIF_Condition_A_Sample_Alpha_02.mzML.quant

[27:51] File #3/6
[27:51] Loading run D:\Proteobench_manuscript_data\LFQ_Orbitrap_AIF_Condition_A_Sample_Alpha_03.mzML
[28:34] 5116692 library precursors are potentially detectable
[28:38] Calibrating with mass accuracies 30 (MS1), 14.0714 (MS2)
[29:14] RT window set to 7.88228
[29:14] Recommended MS1 mass accuracy setting: 8.59691 ppm
[32:43] Removing low confidence identifications
[34:22] Precursors at 1% peptidoform FDR: 52564
[34:23] Removing interfering precursors
[34:27] Training neural networks on 194855 PSMs
[34:33] Number of IDs at 0.01 FDR: 72535
[34:36] Precursors at 1% peptidoform FDR: 68383
[34:36] Calculating protein q-values
[34:36] Number of genes identified at 1% FDR: 0 (precursor-level), 0 (protein-level) (inference performed using proteotypic peptides only)
[34:36] Quantification
[34:37] Precursors with monitored PTMs at 1% FDR: 4262 out of 14440 considered
[34:37] Unmodified precursors with monitored PTM sites at 1% FDR: 8907
[34:37] Precursors with PTMs localised (when required) with > 90% confidence: 4178 out of 4262
[34:37] Quantification information saved to D:\Proteobench_manuscript_data\LFQ_Orbitrap_AIF_Condition_A_Sample_Alpha_03.mzML.quant

[34:37] File #4/6
[34:37] Loading run D:\Proteobench_manuscript_data\LFQ_Orbitrap_AIF_Condition_B_Sample_Alpha_01.mzML
[35:20] 5116692 library precursors are potentially detectable
[35:24] Calibrating with mass accuracies 30 (MS1), 14.0714 (MS2)
[36:00] RT window set to 8.47724
[36:00] Recommended MS1 mass accuracy setting: 8.0841 ppm
[39:44] Removing low confidence identifications
[41:26] Precursors at 1% peptidoform FDR: 51060
[41:27] Removing interfering precursors
[41:30] Training neural networks on 185603 PSMs
[41:35] Number of IDs at 0.01 FDR: 68294
[41:38] Precursors at 1% peptidoform FDR: 64649
[41:38] Calculating protein q-values
[41:38] Number of genes identified at 1% FDR: 0 (precursor-level), 0 (protein-level) (inference performed using proteotypic peptides only)
[41:38] Quantification
[41:39] Precursors with monitored PTMs at 1% FDR: 10320 out of 11609 considered
[41:39] Unmodified precursors with monitored PTM sites at 1% FDR: 622
[41:39] Precursors with PTMs localised (when required) with > 90% confidence: 10316 out of 10320
[41:39] Quantification information saved to D:\Proteobench_manuscript_data\LFQ_Orbitrap_AIF_Condition_B_Sample_Alpha_01.mzML.quant

[41:39] File #5/6
[41:39] Loading run D:\Proteobench_manuscript_data\LFQ_Orbitrap_AIF_Condition_B_Sample_Alpha_02.mzML
[42:26] 5116692 library precursors are potentially detectable
[42:30] Calibrating with mass accuracies 30 (MS1), 14.0714 (MS2)
[43:08] RT window set to 7.94176
[43:08] Recommended MS1 mass accuracy setting: 8.6351 ppm
[47:01] Removing low confidence identifications
[48:54] Precursors at 1% peptidoform FDR: 53625
[48:56] Removing interfering precursors
[48:59] Training neural networks on 200669 PSMs
[49:05] Number of IDs at 0.01 FDR: 71135
[49:08] Precursors at 1% peptidoform FDR: 66973
[49:09] Calculating protein q-values
[49:09] Number of genes identified at 1% FDR: 0 (precursor-level), 0 (protein-level) (inference performed using proteotypic peptides only)
[49:09] Quantification
[49:09] Precursors with monitored PTMs at 1% FDR: 10389 out of 12464 considered
[49:09] Unmodified precursors with monitored PTM sites at 1% FDR: 1120
[49:09] Precursors with PTMs localised (when required) with > 90% confidence: 10377 out of 10389
[49:10] Quantification information saved to D:\Proteobench_manuscript_data\LFQ_Orbitrap_AIF_Condition_B_Sample_Alpha_02.mzML.quant

[49:10] File #6/6
[49:10] Loading run D:\Proteobench_manuscript_data\LFQ_Orbitrap_AIF_Condition_B_Sample_Alpha_03.mzML
[49:57] 5116692 library precursors are potentially detectable
[50:01] Calibrating with mass accuracies 30 (MS1), 14.0714 (MS2)
[50:36] RT window set to 8.74273
[50:37] Recommended MS1 mass accuracy setting: 8.95803 ppm
[54:16] Removing low confidence identifications
[56:02] Precursors at 1% peptidoform FDR: 46915
[56:03] Removing interfering precursors
[56:07] Training neural networks on 178829 PSMs
[56:14] Number of IDs at 0.01 FDR: 64715
[56:17] Precursors at 1% peptidoform FDR: 60185
[56:18] Calculating protein q-values
[56:18] Number of genes identified at 1% FDR: 0 (precursor-level), 0 (protein-level) (inference performed using proteotypic peptides only)
[56:18] Quantification
[56:19] Precursors with monitored PTMs at 1% FDR: 9293 out of 10848 considered
[56:19] Unmodified precursors with monitored PTM sites at 1% FDR: 799
[56:19] Precursors with PTMs localised (when required) with > 90% confidence: 9286 out of 9293
[56:19] Quantification information saved to D:\Proteobench_manuscript_data\LFQ_Orbitrap_AIF_Condition_B_Sample_Alpha_03.mzML.quant

[56:19] Cross-run analysis
[56:19] Reading quantification information: 6 files
[56:25] Quantifying peptides
[56:56] Assembling protein groups
[56:59] Quantifying proteins
[56:59] Calculating q-values for protein and gene groups
[57:00] Calculating global q-values for protein and gene groups
[57:00] Protein groups with global q-value <= 0.01: 9020
[57:02] Compressed report saved to D:\Proteobench_manuscript_data\run_output\diann_1.9.2_conservativenn\report-first-pass.parquet. Use R 'arrow' or Python 'PyArrow' package to process
[57:02] Writing report
[57:17] Report saved to D:\Proteobench_manuscript_data\run_output\diann_1.9.2_conservativenn\report-first-pass.tsv.
[57:17] Saving precursor levels matrix
[57:17] Precursor levels matrix (1% precursor and protein group FDR) saved to D:\Proteobench_manuscript_data\run_output\diann_1.9.2_conservativenn\report-first-pass.pr_matrix.tsv.
[57:17] Manifest saved to D:\Proteobench_manuscript_data\run_output\diann_1.9.2_conservativenn\report-first-pass.manifest.txt
[57:17] Stats report saved to D:\Proteobench_manuscript_data\run_output\diann_1.9.2_conservativenn\report-first-pass.stats.tsv
[57:17] Generating spectral library:
[57:19] 94945 target and 978 decoy precursors saved
[57:19] Spectral library saved to C:\DIA-NN\1.9.2\report-lib.parquet

[57:19] Loading spectral library C:\DIA-NN\1.9.2\report-lib.parquet
[57:20] Spectral library loaded: 10796 protein isoforms, 10605 protein groups and 95923 precursors in 86738 elution groups.
[57:20] Loading protein annotations from FASTA D:\Proteobench_manuscript_data\ProteoBenchFASTA_DDAQuantification.fasta
[57:20] Annotating library proteins with information from the FASTA database
[57:20] Protein names missing for some isoforms
[57:20] Gene names missing for some isoforms
[57:20] Library contains 10784 proteins, and 0 genes
[57:21] Initialising library
[57:21] 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

[57:21] File #1/6
[57:21] Loading run D:\Proteobench_manuscript_data\LFQ_Orbitrap_AIF_Condition_A_Sample_Alpha_01.mzML
[58:10] 94945 library precursors are potentially detectable
[58:10] Calibrating with mass accuracies 30 (MS1), 14.0714 (MS2)
[58:11] RT window set to 2.65069
[58:11] Recommended MS1 mass accuracy setting: 8.93495 ppm
[58:15] Removing low confidence identifications
[58:18] Precursors at 1% peptidoform FDR: 64986
[58:19] Removing interfering precursors
[58:19] Training neural networks on 117594 PSMs
[58:22] Number of IDs at 0.01 FDR: 81269
[58:25] Precursors at 1% peptidoform FDR: 72923
[58:25] Calculating protein q-values
[58:25] Number of genes identified at 1% FDR: 0 (precursor-level), 0 (protein-level) (inference performed using proteotypic peptides only)
[58:25] Quantification
[58:25] Precursors with monitored PTMs at 1% FDR: 6813 out of 16545 considered
[58:25] Unmodified precursors with monitored PTM sites at 1% FDR: 8272
[58:25] Precursors with PTMs localised (when required) with > 90% confidence: 6753 out of 6813

[58:25] File #2/6
[58:25] Loading run D:\Proteobench_manuscript_data\LFQ_Orbitrap_AIF_Condition_A_Sample_Alpha_02.mzML
[59:11] 94945 library precursors are potentially detectable
[59:11] Calibrating with mass accuracies 30 (MS1), 14.0714 (MS2)
[59:12] RT window set to 2.53192
[59:12] Recommended MS1 mass accuracy setting: 9.34364 ppm
[59:16] Removing low confidence identifications
[59:19] Precursors at 1% peptidoform FDR: 65950
[59:20] Removing interfering precursors
[59:20] Training neural networks on 117800 PSMs
[59:23] Number of IDs at 0.01 FDR: 81966
[59:26] Precursors at 1% peptidoform FDR: 73128
[59:26] Calculating protein q-values
[59:26] Number of genes identified at 1% FDR: 0 (precursor-level), 0 (protein-level) (inference performed using proteotypic peptides only)
[59:26] Quantification
[59:26] Precursors with monitored PTMs at 1% FDR: 6778 out of 16821 considered
[59:26] Unmodified precursors with monitored PTM sites at 1% FDR: 8443
[59:26] Precursors with PTMs localised (when required) with > 90% confidence: 6725 out of 6778

[59:26] File #3/6
[59:26] Loading run D:\Proteobench_manuscript_data\LFQ_Orbitrap_AIF_Condition_A_Sample_Alpha_03.mzML
[60:11] 94945 library precursors are potentially detectable
[60:11] Calibrating with mass accuracies 30 (MS1), 14.0714 (MS2)
[60:12] RT window set to 2.56088
[60:12] Recommended MS1 mass accuracy setting: 8.70558 ppm
[60:16] Removing low confidence identifications
[60:19] Precursors at 1% peptidoform FDR: 60689
[60:20] Removing interfering precursors
[60:20] Training neural networks on 115191 PSMs
[60:23] Number of IDs at 0.01 FDR: 77345
[60:26] Precursors at 1% peptidoform FDR: 70041
[60:26] Calculating protein q-values
[60:26] Number of genes identified at 1% FDR: 0 (precursor-level), 0 (protein-level) (inference performed using proteotypic peptides only)
[60:26] Quantification
[60:26] Precursors with monitored PTMs at 1% FDR: 6178 out of 15814 considered
[60:26] Unmodified precursors with monitored PTM sites at 1% FDR: 8267
[60:26] Precursors with PTMs localised (when required) with > 90% confidence: 6122 out of 6178

[60:26] File #4/6
[60:26] Loading run D:\Proteobench_manuscript_data\LFQ_Orbitrap_AIF_Condition_B_Sample_Alpha_01.mzML
[61:09] 94945 library precursors are potentially detectable
[61:09] Calibrating with mass accuracies 30 (MS1), 14.0714 (MS2)
[61:10] RT window set to 2.62466
[61:10] Recommended MS1 mass accuracy setting: 8.28916 ppm
[61:14] Removing low confidence identifications
[61:17] Precursors at 1% peptidoform FDR: 58181
[61:18] Removing interfering precursors
[61:18] Training neural networks on 111176 PSMs
[61:21] Number of IDs at 0.01 FDR: 70569
[61:23] Precursors at 1% peptidoform FDR: 63510
[61:23] Calculating protein q-values
[61:23] Number of genes identified at 1% FDR: 0 (precursor-level), 0 (protein-level) (inference performed using proteotypic peptides only)
[61:23] Quantification
[61:23] Precursors with monitored PTMs at 1% FDR: 9095 out of 10940 considered
[61:23] Unmodified precursors with monitored PTM sites at 1% FDR: 784
[61:23] Precursors with PTMs localised (when required) with > 90% confidence: 9084 out of 9095

[61:24] File #5/6
[61:24] Loading run D:\Proteobench_manuscript_data\LFQ_Orbitrap_AIF_Condition_B_Sample_Alpha_02.mzML
[62:05] 94945 library precursors are potentially detectable
[62:05] Calibrating with mass accuracies 30 (MS1), 14.0714 (MS2)
[62:06] RT window set to 2.54759
[62:06] Recommended MS1 mass accuracy setting: 8.93379 ppm
[62:10] Removing low confidence identifications
[62:13] Precursors at 1% peptidoform FDR: 59071
[62:14] Removing interfering precursors
[62:14] Training neural networks on 112588 PSMs
[62:17] Number of IDs at 0.01 FDR: 72180
[62:19] Precursors at 1% peptidoform FDR: 65142
[62:19] Calculating protein q-values
[62:19] Number of genes identified at 1% FDR: 0 (precursor-level), 0 (protein-level) (inference performed using proteotypic peptides only)
[62:19] Quantification
[62:20] Precursors with monitored PTMs at 1% FDR: 9237 out of 11931 considered
[62:20] Unmodified precursors with monitored PTM sites at 1% FDR: 1569
[62:20] Precursors with PTMs localised (when required) with > 90% confidence: 9206 out of 9237

[62:20] File #6/6
[62:20] Loading run D:\Proteobench_manuscript_data\LFQ_Orbitrap_AIF_Condition_B_Sample_Alpha_03.mzML
[63:02] 94945 library precursors are potentially detectable
[63:02] Calibrating with mass accuracies 30 (MS1), 14.0714 (MS2)
[63:03] RT window set to 2.61206
[63:03] Recommended MS1 mass accuracy setting: 9.54626 ppm
[63:06] Removing low confidence identifications
[63:09] Precursors at 1% peptidoform FDR: 54458
[63:09] Removing interfering precursors
[63:10] Training neural networks on 109319 PSMs
[63:12] Number of IDs at 0.01 FDR: 68425
[63:15] Precursors at 1% peptidoform FDR: 62179
[63:15] Calculating protein q-values
[63:15] Number of genes identified at 1% FDR: 0 (precursor-level), 0 (protein-level) (inference performed using proteotypic peptides only)
[63:15] Quantification
[63:15] Precursors with monitored PTMs at 1% FDR: 8952 out of 11200 considered
[63:15] Unmodified precursors with monitored PTM sites at 1% FDR: 1224
[63:15] Precursors with PTMs localised (when required) with > 90% confidence: 8921 out of 8952

[63:15] Cross-run analysis
[63:15] Reading quantification information: 6 files
[63:16] Quantifying peptides
[64:27] Quantification parameters: 0.330971, 0.00172603, 0.000554021, 0.0115435, 0.0129397, 0.011917, 0.0899186, 0.0750107, 0.126449, 0.0137775, 0.047, 0.0326632, 0.451363, 0.0502325, 0.0584348, 0.0105487
[64:36] Quantifying proteins
[64:36] Calculating q-values for protein and gene groups
[64:36] Calculating global q-values for protein and gene groups
[64:36] Protein groups with global q-value <= 0.01: 8806
[64:37] Compressed report saved to D:\Proteobench_manuscript_data\run_output\diann_1.9.2_conservativenn\report.parquet. Use R 'arrow' or Python 'PyArrow' package to process
[64:37] Writing report
[64:48] Report saved to D:\Proteobench_manuscript_data\run_output\diann_1.9.2_conservativenn\report.tsv.
[64:48] Saving precursor levels matrix
[64:48] Precursor levels matrix (1% precursor and protein group FDR) saved to D:\Proteobench_manuscript_data\run_output\diann_1.9.2_conservativenn\report.pr_matrix.tsv.
[64:48] Saving protein group levels matrix
[64:48] Protein group levels matrix (1% precursor FDR and protein group FDR) saved to D:\Proteobench_manuscript_data\run_output\diann_1.9.2_conservativenn\report.pg_matrix.tsv.
[64:48] Saving gene group levels matrix
[64:48] Gene groups levels matrix (1% precursor FDR and protein group FDR) saved to D:\Proteobench_manuscript_data\run_output\diann_1.9.2_conservativenn\report.gg_matrix.tsv.
[64:48] Saving unique genes levels matrix
[64:48] Unique genes levels matrix (1% precursor FDR and protein group FDR) saved to D:\Proteobench_manuscript_data\run_output\diann_1.9.2_conservativenn\report.unique_genes_matrix.tsv.
[64:48] Manifest saved to D:\Proteobench_manuscript_data\run_output\diann_1.9.2_conservativenn\report.manifest.txt
[64:48] Stats report saved to D:\Proteobench_manuscript_data\run_output\diann_1.9.2_conservativenn\report.stats.tsv

