DIA-NN 1.9.1 (Data-Independent Acquisition by Neural Networks)
Compiled on Jul 15 2024 15:40:36
Current date and time: Thu Nov  7 14:41:19 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_1.9.1_default\report.tsv --qvalue 0.01 --matrices --out-lib C:\DIA-NN\1.9.1\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
[0:10] Assembling elution groups
[0:14] 5116692 precursors generated
[0:14] Protein names missing for some isoforms
[0:14] Gene names missing for some isoforms
[0:14] Library contains 31685 proteins, and 0 genes
[0:17] [0:22] [9:42] [11:56] [12:01] [12:02] Saving the library to C:\DIA-NN\1.9.1\report-lib.predicted.speclib
[12:27] Initialising library
[12:40] Loading spectral library C:\DIA-NN\1.9.1\report-lib.predicted.speclib
[12:54] Library annotated with sequence database(s): D:\Proteobench_manuscript_data\ProteoBenchFASTA_DDAQuantification.fasta
[12:55] Spectral library loaded: 31837 protein isoforms, 51765 protein groups and 5116692 precursors in 2716663 elution groups.
[12:55] Loading protein annotations from FASTA D:\Proteobench_manuscript_data\ProteoBenchFASTA_DDAQuantification.fasta
[12:55] Annotating library proteins with information from the FASTA database
[12:55] Protein names missing for some isoforms
[12:55] Gene names missing for some isoforms
[12:55] Library contains 31685 proteins, and 0 genes
[13:00] [13:10] [24:34] [25:54] [25:58] [25:59] Saving the library to C:\DIA-NN\1.9.1\report-lib.predicted.speclib
[26:11] Initialising library

First pass: generating a spectral library from DIA data

[26:17] File #1/6
[26:17] Loading run D:\Proteobench_manuscript_data\LFQ_Orbitrap_AIF_Condition_A_Sample_Alpha_01.mzML
[27:04] 5116692 library precursors are potentially detectable
[27:08] Processing...
[27:30] RT window set to 6.01245
[27:30] Peak width: 6.352
[27:30] Scan window radius set to 13
[27:31] Recommended MS1 mass accuracy setting: 7.67047 ppm
[28:03] Optimised mass accuracy: 15.9405 ppm
[31:14] Removing low confidence identifications
[32:45] Precursors at 1% peptidoform FDR: 56728
[32:47] Removing interfering precursors
[32:54] Training neural networks: 122889 targets, 84843 decoys
[33:02] Number of IDs at 0.01 FDR: 95310
[33:16] Precursors at 1% peptidoform FDR: 69916
[33:17] Calculating protein q-values
[33:17] Number of genes identified at 1% FDR: 0 (precursor-level), 0 (protein-level) (inference performed using proteotypic peptides only)
[33:17] Quantification
[33:18] Precursors with monitored PTMs at 1% FDR: 4829 out of 30522 considered
[33:18] Unmodified precursors with monitored PTM sites at 1% FDR: 8006
[33:18] Precursors with PTMs localised (when required) with > 90% confidence: 4748 out of 4829
[33:22] Quantification information saved to D:\Proteobench_manuscript_data\LFQ_Orbitrap_AIF_Condition_A_Sample_Alpha_01.mzML.quant

[33:22] File #2/6
[33:22] Loading run D:\Proteobench_manuscript_data\LFQ_Orbitrap_AIF_Condition_A_Sample_Alpha_02.mzML
[34:10] 5116692 library precursors are potentially detectable
[34:14] Processing...
[34:40] RT window set to 6.52086
[34:40] Recommended MS1 mass accuracy setting: 8.46976 ppm
[38:13] Removing low confidence identifications
[40:01] Precursors at 1% peptidoform FDR: 61542
[40:02] Removing interfering precursors
[40:09] Training neural networks: 135917 targets, 93821 decoys
[40:18] Number of IDs at 0.01 FDR: 102890
[40:33] Precursors at 1% peptidoform FDR: 72122
[40:33] Calculating protein q-values
[40:34] Number of genes identified at 1% FDR: 0 (precursor-level), 0 (protein-level) (inference performed using proteotypic peptides only)
[40:34] Quantification
[40:35] Precursors with monitored PTMs at 1% FDR: 4680 out of 36032 considered
[40:35] Unmodified precursors with monitored PTM sites at 1% FDR: 9343
[40:35] Precursors with PTMs localised (when required) with > 90% confidence: 4617 out of 4680
[40:36] Quantification information saved to D:\Proteobench_manuscript_data\LFQ_Orbitrap_AIF_Condition_A_Sample_Alpha_02.mzML.quant

[40:37] File #3/6
[40:37] Loading run D:\Proteobench_manuscript_data\LFQ_Orbitrap_AIF_Condition_A_Sample_Alpha_03.mzML
[41:35] 5116692 library precursors are potentially detectable
[41:39] Processing...
[42:10] RT window set to 6.12928
[42:10] Recommended MS1 mass accuracy setting: 8.6532 ppm
[44:42] Removing low confidence identifications
[45:44] Precursors at 1% peptidoform FDR: 55210
[45:45] Removing interfering precursors
[45:48] Training neural networks: 124261 targets, 85854 decoys
[45:54] Number of IDs at 0.01 FDR: 92412
[46:01] Precursors at 1% peptidoform FDR: 65684
[46:02] Calculating protein q-values
[46:02] Number of genes identified at 1% FDR: 0 (precursor-level), 0 (protein-level) (inference performed using proteotypic peptides only)
[46:02] Quantification
[46:02] Precursors with monitored PTMs at 1% FDR: 4244 out of 31591 considered
[46:02] Unmodified precursors with monitored PTM sites at 1% FDR: 8314
[46:02] Precursors with PTMs localised (when required) with > 90% confidence: 4180 out of 4244
[46:03] Quantification information saved to D:\Proteobench_manuscript_data\LFQ_Orbitrap_AIF_Condition_A_Sample_Alpha_03.mzML.quant

[46:03] File #4/6
[46:03] Loading run D:\Proteobench_manuscript_data\LFQ_Orbitrap_AIF_Condition_B_Sample_Alpha_01.mzML
[46:52] 5116692 library precursors are potentially detectable
[46:55] Processing...
[47:19] RT window set to 7.2009
[47:19] Recommended MS1 mass accuracy setting: 7.63561 ppm
[49:52] Removing low confidence identifications
[51:05] Precursors at 1% peptidoform FDR: 52672
[51:06] Removing interfering precursors
[51:09] Training neural networks: 120568 targets, 82029 decoys
[51:14] Number of IDs at 0.01 FDR: 87386
[51:22] Precursors at 1% peptidoform FDR: 62086
[51:22] Calculating protein q-values
[51:22] Number of genes identified at 1% FDR: 0 (precursor-level), 0 (protein-level) (inference performed using proteotypic peptides only)
[51:22] Quantification
[51:23] Precursors with monitored PTMs at 1% FDR: 9045 out of 26941 considered
[51:23] Unmodified precursors with monitored PTM sites at 1% FDR: 572
[51:23] Precursors with PTMs localised (when required) with > 90% confidence: 9041 out of 9045
[51:24] Quantification information saved to D:\Proteobench_manuscript_data\LFQ_Orbitrap_AIF_Condition_B_Sample_Alpha_01.mzML.quant

[51:24] File #5/6
[51:24] Loading run D:\Proteobench_manuscript_data\LFQ_Orbitrap_AIF_Condition_B_Sample_Alpha_02.mzML
[52:13] 5116692 library precursors are potentially detectable
[52:16] Processing...
[52:39] RT window set to 6.63492
[52:40] Recommended MS1 mass accuracy setting: 8.73728 ppm
[55:23] Removing low confidence identifications
[56:40] Precursors at 1% peptidoform FDR: 54666
[56:41] Removing interfering precursors
[56:44] Training neural networks: 126597 targets, 85363 decoys
[56:50] Number of IDs at 0.01 FDR: 91934
[56:57] Precursors at 1% peptidoform FDR: 63972
[56:58] Calculating protein q-values
[56:58] Number of genes identified at 1% FDR: 0 (precursor-level), 0 (protein-level) (inference performed using proteotypic peptides only)
[56:58] Quantification
[56:58] Precursors with monitored PTMs at 1% FDR: 9068 out of 29654 considered
[56:58] Unmodified precursors with monitored PTM sites at 1% FDR: 1032
[56:58] Precursors with PTMs localised (when required) with > 90% confidence: 9057 out of 9068
[56:59] Quantification information saved to D:\Proteobench_manuscript_data\LFQ_Orbitrap_AIF_Condition_B_Sample_Alpha_02.mzML.quant

[56:59] File #6/6
[56:59] Loading run D:\Proteobench_manuscript_data\LFQ_Orbitrap_AIF_Condition_B_Sample_Alpha_03.mzML
[57:47] 5116692 library precursors are potentially detectable
[57:50] Processing...
[58:13] RT window set to 7.18162
[58:13] Recommended MS1 mass accuracy setting: 8.70507 ppm
[60:34] Removing low confidence identifications
[61:41] Precursors at 1% peptidoform FDR: 49112
[61:42] Removing interfering precursors
[61:45] Training neural networks: 113812 targets, 77893 decoys
[61:50] Number of IDs at 0.01 FDR: 82547
[61:57] Precursors at 1% peptidoform FDR: 58431
[61:58] Calculating protein q-values
[61:58] Number of genes identified at 1% FDR: 0 (precursor-level), 0 (protein-level) (inference performed using proteotypic peptides only)
[61:58] Quantification
[61:58] Precursors with monitored PTMs at 1% FDR: 8047 out of 25813 considered
[61:58] Unmodified precursors with monitored PTM sites at 1% FDR: 773
[61:58] Precursors with PTMs localised (when required) with > 90% confidence: 8044 out of 8047
[62:00] Quantification information saved to D:\Proteobench_manuscript_data\LFQ_Orbitrap_AIF_Condition_B_Sample_Alpha_03.mzML.quant

[62:00] Cross-run analysis
[62:00] Reading quantification information: 6 files
[62:05] Quantifying peptides
[62:28] Assembling protein groups
[62:29] Quantifying proteins
[62:29] Calculating q-values for protein and gene groups
[62:30] Calculating global q-values for protein and gene groups
[62:30] Protein groups with global q-value <= 0.01: 23171
[62:32] Compressed report saved to D:\Proteobench_manuscript_data\run_output\diann_1.9.1_default\report-first-pass.parquet. Use R 'arrow' or Python 'PyArrow' package to process
[62:32] Writing report
[62:45] Report saved to D:\Proteobench_manuscript_data\run_output\diann_1.9.1_default\report-first-pass.tsv.
[62:45] Saving precursor levels matrix
[62:45] Precursor levels matrix (1% precursor and protein group FDR) saved to D:\Proteobench_manuscript_data\run_output\diann_1.9.1_default\report-first-pass.pr_matrix.tsv.
[62:45] Manifest saved to D:\Proteobench_manuscript_data\run_output\diann_1.9.1_default\report-first-pass.manifest.txt
[62:45] Stats report saved to D:\Proteobench_manuscript_data\run_output\diann_1.9.1_default\report-first-pass.stats.tsv
[62:45] Generating spectral library:
[62:47] 154037 target and 1586 decoy precursors saved
[62:47] Spectral library saved to C:\DIA-NN\1.9.1\report-lib.parquet

[62:48] Loading spectral library C:\DIA-NN\1.9.1\report-lib.parquet
[62:49] Spectral library loaded: 24085 protein isoforms, 23691 protein groups and 155623 precursors in 145774 elution groups.
[62:49] Loading protein annotations from FASTA D:\Proteobench_manuscript_data\ProteoBenchFASTA_DDAQuantification.fasta
[62:49] Annotating library proteins with information from the FASTA database
[62:49] Protein names missing for some isoforms
[62:49] Gene names missing for some isoforms
[62:49] Library contains 24038 proteins, and 0 genes
[62:49] Initialising library
[62:50] Saving the library to C:\DIA-NN\1.9.1\report-lib.parquet.skyline.speclib


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

[62:50] File #1/6
[62:50] Loading run D:\Proteobench_manuscript_data\LFQ_Orbitrap_AIF_Condition_A_Sample_Alpha_01.mzML
[63:32] 154037 library precursors are potentially detectable
[63:32] Processing...
[63:34] RT window set to 2.64868
[63:34] Recommended MS1 mass accuracy setting: 7.71037 ppm
[63:38] Removing low confidence identifications
[63:40] Precursors at 1% peptidoform FDR: 61861
[63:41] Removing interfering precursors
[63:42] Training neural networks: 110207 targets, 63536 decoys
[63:46] Number of IDs at 0.01 FDR: 88453
[63:50] Precursors at 1% peptidoform FDR: 72849
[63:50] Calculating protein q-values
[63:50] Number of genes identified at 1% FDR: 0 (precursor-level), 0 (protein-level) (inference performed using proteotypic peptides only)
[63:50] Quantification
[63:51] Precursors with monitored PTMs at 1% FDR: 6709 out of 24342 considered
[63:51] Unmodified precursors with monitored PTM sites at 1% FDR: 8190
[63:51] Precursors with PTMs localised (when required) with > 90% confidence: 6646 out of 6709

[63:51] File #2/6
[63:51] Loading run D:\Proteobench_manuscript_data\LFQ_Orbitrap_AIF_Condition_A_Sample_Alpha_02.mzML
[64:35] 154037 library precursors are potentially detectable
[64:35] Processing...
[64:37] RT window set to 2.57547
[64:37] Recommended MS1 mass accuracy setting: 8.407 ppm
[64:42] Removing low confidence identifications
[64:44] Precursors at 1% peptidoform FDR: 61396
[64:45] Removing interfering precursors
[64:45] Training neural networks: 112343 targets, 64380 decoys
[64:49] Number of IDs at 0.01 FDR: 91153
[64:54] Precursors at 1% peptidoform FDR: 73687
[64:54] Calculating protein q-values
[64:54] Number of genes identified at 1% FDR: 0 (precursor-level), 0 (protein-level) (inference performed using proteotypic peptides only)
[64:54] Quantification
[64:54] Precursors with monitored PTMs at 1% FDR: 6702 out of 26542 considered
[64:54] Unmodified precursors with monitored PTM sites at 1% FDR: 8569
[64:54] Precursors with PTMs localised (when required) with > 90% confidence: 6654 out of 6702

[64:54] File #3/6
[64:54] Loading run D:\Proteobench_manuscript_data\LFQ_Orbitrap_AIF_Condition_A_Sample_Alpha_03.mzML
[65:41] 154037 library precursors are potentially detectable
[65:41] Processing...
[65:42] RT window set to 2.58331
[65:42] Recommended MS1 mass accuracy setting: 7.2481 ppm
[65:47] Removing low confidence identifications
[65:49] Precursors at 1% peptidoform FDR: 57658
[65:49] Removing interfering precursors
[65:50] Training neural networks: 107589 targets, 61630 decoys
[65:54] Number of IDs at 0.01 FDR: 84987
[65:59] Precursors at 1% peptidoform FDR: 69581
[65:59] Calculating protein q-values
[65:59] Number of genes identified at 1% FDR: 0 (precursor-level), 0 (protein-level) (inference performed using proteotypic peptides only)
[65:59] Quantification
[65:59] Precursors with monitored PTMs at 1% FDR: 6095 out of 24755 considered
[65:59] Unmodified precursors with monitored PTM sites at 1% FDR: 8263
[65:59] Precursors with PTMs localised (when required) with > 90% confidence: 6033 out of 6095

[65:59] File #4/6
[65:59] Loading run D:\Proteobench_manuscript_data\LFQ_Orbitrap_AIF_Condition_B_Sample_Alpha_01.mzML
[66:44] 154037 library precursors are potentially detectable
[66:44] Processing...
[66:45] RT window set to 2.63141
[66:45] Recommended MS1 mass accuracy setting: 7.25062 ppm
[66:50] Removing low confidence identifications
[66:51] Precursors at 1% peptidoform FDR: 54118
[66:52] Removing interfering precursors
[66:53] Training neural networks: 106216 targets, 60209 decoys
[66:57] Number of IDs at 0.01 FDR: 78767
[67:01] Precursors at 1% peptidoform FDR: 63510
[67:01] Calculating protein q-values
[67:01] Number of genes identified at 1% FDR: 0 (precursor-level), 0 (protein-level) (inference performed using proteotypic peptides only)
[67:01] Quantification
[67:01] Precursors with monitored PTMs at 1% FDR: 8970 out of 19465 considered
[67:01] Unmodified precursors with monitored PTM sites at 1% FDR: 797
[67:01] Precursors with PTMs localised (when required) with > 90% confidence: 8950 out of 8970

[67:01] File #5/6
[67:01] Loading run D:\Proteobench_manuscript_data\LFQ_Orbitrap_AIF_Condition_B_Sample_Alpha_02.mzML
[67:46] 154037 library precursors are potentially detectable
[67:46] Processing...
[67:48] RT window set to 2.5758
[67:48] Recommended MS1 mass accuracy setting: 8.3537 ppm
[67:52] Removing low confidence identifications
[67:55] Precursors at 1% peptidoform FDR: 56111
[67:55] Removing interfering precursors
[67:56] Training neural networks: 109095 targets, 61956 decoys
[68:00] Number of IDs at 0.01 FDR: 81266
[68:04] Precursors at 1% peptidoform FDR: 64334
[68:04] Calculating protein q-values
[68:04] Number of genes identified at 1% FDR: 0 (precursor-level), 0 (protein-level) (inference performed using proteotypic peptides only)
[68:04] Quantification
[68:04] Precursors with monitored PTMs at 1% FDR: 9099 out of 22391 considered
[68:04] Unmodified precursors with monitored PTM sites at 1% FDR: 1426
[68:04] Precursors with PTMs localised (when required) with > 90% confidence: 9068 out of 9099

[68:05] File #6/6
[68:05] Loading run D:\Proteobench_manuscript_data\LFQ_Orbitrap_AIF_Condition_B_Sample_Alpha_03.mzML
[68:49] 154037 library precursors are potentially detectable
[68:49] Processing...
[68:50] RT window set to 2.61012
[68:50] Recommended MS1 mass accuracy setting: 7.45381 ppm
[68:54] Removing low confidence identifications
[68:56] Precursors at 1% peptidoform FDR: 51580
[68:57] Removing interfering precursors
[68:57] Training neural networks: 103774 targets, 59068 decoys
[69:01] Number of IDs at 0.01 FDR: 75751
[69:06] Precursors at 1% peptidoform FDR: 61037
[69:06] Calculating protein q-values
[69:06] Number of genes identified at 1% FDR: 0 (precursor-level), 0 (protein-level) (inference performed using proteotypic peptides only)
[69:06] Quantification
[69:06] Precursors with monitored PTMs at 1% FDR: 8799 out of 20445 considered
[69:06] Unmodified precursors with monitored PTM sites at 1% FDR: 1089
[69:06] Precursors with PTMs localised (when required) with > 90% confidence: 8762 out of 8799

[69:06] Cross-run analysis
[69:06] Reading quantification information: 6 files
[69:08] Quantifying peptides
[70:28] Quantification parameters: 0.350185, 0.00166602, 0.000542271, 0.0182992, 0.0679423, 0.062612, 0.0110183, 0.0142922, 0.0128172, 0.0394645, 0.049644, 0.0480817, 0.388546, 0.050192, 0.0569354, 0.0116481
[70:38] Quantifying proteins
[70:39] Calculating q-values for protein and gene groups
[70:39] Calculating global q-values for protein and gene groups
[70:39] Protein groups with global q-value <= 0.01: 17335
[70:41] Compressed report saved to D:\Proteobench_manuscript_data\run_output\diann_1.9.1_default\report.parquet. Use R 'arrow' or Python 'PyArrow' package to process
[70:41] Writing report
[70:52] Report saved to D:\Proteobench_manuscript_data\run_output\diann_1.9.1_default\report.tsv.
[70:52] Saving precursor levels matrix
[70:53] Precursor levels matrix (1% precursor and protein group FDR) saved to D:\Proteobench_manuscript_data\run_output\diann_1.9.1_default\report.pr_matrix.tsv.
[70:53] Saving protein group levels matrix
[70:53] Protein group levels matrix (1% precursor FDR and protein group FDR) saved to D:\Proteobench_manuscript_data\run_output\diann_1.9.1_default\report.pg_matrix.tsv.
[70:53] Saving gene group levels matrix
[70:53] Gene groups levels matrix (1% precursor FDR and protein group FDR) saved to D:\Proteobench_manuscript_data\run_output\diann_1.9.1_default\report.gg_matrix.tsv.
[70:53] Saving unique genes levels matrix
[70:53] Unique genes levels matrix (1% precursor FDR and protein group FDR) saved to D:\Proteobench_manuscript_data\run_output\diann_1.9.1_default\report.unique_genes_matrix.tsv.
[70:53] Manifest saved to D:\Proteobench_manuscript_data\run_output\diann_1.9.1_default\report.manifest.txt
[70:53] Stats report saved to D:\Proteobench_manuscript_data\run_output\diann_1.9.1_default\report.stats.tsv

