DIA-NN 1.8 (Data-Independent Acquisition by Neural Networks)
Compiled on Jun 28 2021 14:55:31
Current date and time: Thu Oct 31 12:23:32 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_1.8_default\report.tsv --qvalue 0.01 --matrices --out-lib D:\Proteobench_manuscript_data\run_output\diann_1.8_default\report-lib.tsv --gen-spec-lib --predictor --fasta D:\Proteobench_manuscript_data\ProteoBenchFASTA_DDAQuantification.fasta --fasta-search --min-fr-mz 50 --max-fr-mz 2000 --met-excision --cut K*,R* --missed-cleavages 1 --min-pep-len 6 --max-pep-len 30 --min-pr-mz 400 --max-pr-mz 1000 --min-pr-charge 1 --max-pr-charge 4 --unimod4 --var-mods 1 --var-mod UniMod:35,15.994915,M --var-mod UniMod:1,42.010565,*n --monitor-mod UniMod:1 --reanalyse --smart-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
Library-free search enabled
Min fragment m/z set to 50
Max fragment m/z set to 2000
N-terminal methionine excision enabled
In silico digest will involve cuts at K*,R*
Maximum number of missed cleavages set to 1
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
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
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
When generating a spectral library, in silico predicted spectra will be retained if deemed more reliable than experimental ones
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.
The following variable modifications will be scored: UniMod:1 

6 files will be processed
[0:00] Loading FASTA D:\Proteobench_manuscript_data\ProteoBenchFASTA_DDAQuantification.fasta
[0:04] Processing FASTA
[0:11] Assembling elution groups
[0:16] 4880109 precursors generated
[0:16] Protein names missing for some isoforms
[0:16] Gene names missing for some isoforms
[0:16] Library contains 31676 proteins, and 0 genes
[0:17] [0:22] [11:20] [14:03] [14:08] [14:10] Saving the library to D:\Proteobench_manuscript_data\run_output\diann_1.8_default\report-lib.predicted.speclib
[18:16] Initialising library

[18:18] First pass: generating a spectral library from DIA data
[18:18] File #1/6
[18:18] Loading run D:\Proteobench_manuscript_data\LFQ_Orbitrap_AIF_Condition_A_Sample_Alpha_01.mzML
[19:10] 4880109 library precursors are potentially detectable
[19:11] Processing...
[19:49] RT window set to 10.0939
[19:49] Peak width: 6.24
[19:49] Scan window radius set to 13
[19:49] Recommended MS1 mass accuracy setting: 9.12019 ppm
[21:10] Optimised mass accuracy: 31.5257 ppm
[27:03] Removing low confidence identifications
[27:03] Searching PTM decoys
[27:07] Removing interfering precursors
[27:10] Training neural networks: 135398 targets, 117167 decoys
[27:15] Number of IDs at 0.01 FDR: 82381
[27:15] Calculating protein q-values
[27:16] Number of genes identified at 1% FDR: 0 (precursor-level), 0 (protein-level) (inference performed using proteotypic peptides only)
[27:16] Quantification
[27:16] Precursors with monitored PTMs at 1% FDR: 803 out of 882
[27:16] Unmodified precursors with monitored PTM sites at 1% FDR: 309 out of 349
[27:16] Quantification information saved to D:\Proteobench_manuscript_data\LFQ_Orbitrap_AIF_Condition_A_Sample_Alpha_01.mzML.quant.

[27:18] File #2/6
[27:18] Loading run D:\Proteobench_manuscript_data\LFQ_Orbitrap_AIF_Condition_A_Sample_Alpha_02.mzML
[28:04] 4880109 library precursors are potentially detectable
[28:04] Processing...
[28:38] RT window set to 10.4379
[28:38] Recommended MS1 mass accuracy setting: 9.65274 ppm
[35:06] Removing low confidence identifications
[35:07] Searching PTM decoys
[35:11] Removing interfering precursors
[35:14] Training neural networks: 138392 targets, 118738 decoys
[35:19] Number of IDs at 0.01 FDR: 85143
[35:20] Calculating protein q-values
[35:20] Number of genes identified at 1% FDR: 0 (precursor-level), 0 (protein-level) (inference performed using proteotypic peptides only)
[35:20] Quantification
[35:21] Precursors with monitored PTMs at 1% FDR: 869 out of 927
[35:21] Unmodified precursors with monitored PTM sites at 1% FDR: 337 out of 361
[35:21] Quantification information saved to D:\Proteobench_manuscript_data\LFQ_Orbitrap_AIF_Condition_A_Sample_Alpha_02.mzML.quant.

[35:22] File #3/6
[35:22] Loading run D:\Proteobench_manuscript_data\LFQ_Orbitrap_AIF_Condition_A_Sample_Alpha_03.mzML
[36:11] 4880109 library precursors are potentially detectable
[36:11] Processing...
[36:50] RT window set to 9.86564
[36:51] Recommended MS1 mass accuracy setting: 9.47395 ppm
[42:39] Removing low confidence identifications
[42:40] Searching PTM decoys
[42:45] Removing interfering precursors
[42:48] Training neural networks: 124859 targets, 104040 decoys
[42:54] Number of IDs at 0.01 FDR: 76657
[42:55] Calculating protein q-values
[42:56] Number of genes identified at 1% FDR: 0 (precursor-level), 0 (protein-level) (inference performed using proteotypic peptides only)
[42:56] Quantification
[42:56] Precursors with monitored PTMs at 1% FDR: 779 out of 843
[42:56] Unmodified precursors with monitored PTM sites at 1% FDR: 289 out of 314
[42:57] Quantification information saved to D:\Proteobench_manuscript_data\LFQ_Orbitrap_AIF_Condition_A_Sample_Alpha_03.mzML.quant.

[42:58] File #4/6
[42:58] Loading run D:\Proteobench_manuscript_data\LFQ_Orbitrap_AIF_Condition_B_Sample_Alpha_01.mzML
[44:03] 4880109 library precursors are potentially detectable
[44:03] Processing...
[45:02] RT window set to 10.0182
[45:03] Recommended MS1 mass accuracy setting: 9.09317 ppm
[52:44] Removing low confidence identifications
[52:44] Searching PTM decoys
[52:50] Removing interfering precursors
[52:53] Training neural networks: 120418 targets, 103448 decoys
[52:59] Number of IDs at 0.01 FDR: 72058
[53:00] Calculating protein q-values
[53:00] Number of genes identified at 1% FDR: 0 (precursor-level), 0 (protein-level) (inference performed using proteotypic peptides only)
[53:01] Quantification
[53:01] Precursors with monitored PTMs at 1% FDR: 575 out of 578
[53:01] Unmodified precursors with monitored PTM sites at 1% FDR: 315 out of 316
[53:02] Quantification information saved to D:\Proteobench_manuscript_data\LFQ_Orbitrap_AIF_Condition_B_Sample_Alpha_01.mzML.quant.

[53:03] File #5/6
[53:03] Loading run D:\Proteobench_manuscript_data\LFQ_Orbitrap_AIF_Condition_B_Sample_Alpha_02.mzML
[54:19] 4880109 library precursors are potentially detectable
[54:20] Processing...
[55:20] RT window set to 10.4217
[55:21] Recommended MS1 mass accuracy setting: 9.9702 ppm
[62:48] Removing low confidence identifications
[62:48] Searching PTM decoys
[62:55] Removing interfering precursors
[63:00] Training neural networks: 122424 targets, 105272 decoys
[63:09] Number of IDs at 0.01 FDR: 75349
[63:10] Calculating protein q-values
[63:11] Number of genes identified at 1% FDR: 0 (precursor-level), 0 (protein-level) (inference performed using proteotypic peptides only)
[63:11] Quantification
[63:12] Precursors with monitored PTMs at 1% FDR: 619 out of 630
[63:12] Unmodified precursors with monitored PTM sites at 1% FDR: 342 out of 351
[63:13] Quantification information saved to D:\Proteobench_manuscript_data\LFQ_Orbitrap_AIF_Condition_B_Sample_Alpha_02.mzML.quant.

[63:16] File #6/6
[63:16] Loading run D:\Proteobench_manuscript_data\LFQ_Orbitrap_AIF_Condition_B_Sample_Alpha_03.mzML
[64:31] 4880109 library precursors are potentially detectable
[64:32] Processing...
[65:34] RT window set to 10.9158
[65:35] Recommended MS1 mass accuracy setting: 10.032 ppm
[73:26] Removing low confidence identifications
[73:26] Searching PTM decoys
[73:32] Removing interfering precursors
[73:37] Training neural networks: 117666 targets, 98918 decoys
[73:45] Number of IDs at 0.01 FDR: 68539
[73:46] Calculating protein q-values
[73:47] Number of genes identified at 1% FDR: 0 (precursor-level), 0 (protein-level) (inference performed using proteotypic peptides only)
[73:47] Quantification
[73:48] Precursors with monitored PTMs at 1% FDR: 561 out of 618
[73:48] Unmodified precursors with monitored PTM sites at 1% FDR: 281 out of 318
[73:49] Quantification information saved to D:\Proteobench_manuscript_data\LFQ_Orbitrap_AIF_Condition_B_Sample_Alpha_03.mzML.quant.

[73:51] Cross-run analysis
[73:51] Reading quantification information: 6 files
[73:53] Quantifying peptides
[74:01] Assembling protein groups
[74:08] Quantifying proteins
[74:08] Calculating q-values for protein and gene groups
[74:09] Calculating global q-values for protein and gene groups
[74:10] Writing report
[74:42] Report saved to D:\Proteobench_manuscript_data\run_output\diann_1.8_default\report-first-pass.tsv.
[74:42] Saving precursor levels matrix
[74:43] Precursor levels matrix (1% precursor and protein group FDR) saved to D:\Proteobench_manuscript_data\run_output\diann_1.8_default\report-first-pass.pr_matrix.tsv.
[74:43] Saving protein group levels matrix
[74:43] Protein group levels matrix (1% precursor FDR and protein group FDR) saved to D:\Proteobench_manuscript_data\run_output\diann_1.8_default\report-first-pass.pg_matrix.tsv.
[74:43] Saving gene group levels matrix
[74:43] Gene groups levels matrix (1% precursor FDR and protein group FDR) saved to D:\Proteobench_manuscript_data\run_output\diann_1.8_default\report-first-pass.gg_matrix.tsv.
[74:43] Saving unique genes levels matrix
[74:43] Unique genes levels matrix (1% precursor FDR and protein group FDR) saved to D:\Proteobench_manuscript_data\run_output\diann_1.8_default\report-first-pass.unique_genes_matrix.tsv.
[74:43] Stats report saved to D:\Proteobench_manuscript_data\run_output\diann_1.8_default\report-first-pass.stats.tsv
[74:43] Generating spectral library:
[74:43] Reading quantification information: 6 files
[74:45] Assembling protein groups
[74:51] 107668 precursors passing the FDR threshold are to be extracted
[74:51] Loading run D:\Proteobench_manuscript_data\LFQ_Orbitrap_AIF_Condition_A_Sample_Alpha_01.mzML
[75:56] 4880109 library precursors are potentially detectable
[75:58] 19656 spectra added to the library
[75:59] Loading run D:\Proteobench_manuscript_data\LFQ_Orbitrap_AIF_Condition_A_Sample_Alpha_02.mzML
[77:02] 4880109 library precursors are potentially detectable
[77:04] 22333 spectra added to the library
[77:05] Loading run D:\Proteobench_manuscript_data\LFQ_Orbitrap_AIF_Condition_A_Sample_Alpha_03.mzML
[78:19] 4880109 library precursors are potentially detectable
[78:20] 5185 spectra added to the library
[78:22] Loading run D:\Proteobench_manuscript_data\LFQ_Orbitrap_AIF_Condition_B_Sample_Alpha_01.mzML
[79:39] 4880109 library precursors are potentially detectable
[79:41] 16065 spectra added to the library
[79:43] Loading run D:\Proteobench_manuscript_data\LFQ_Orbitrap_AIF_Condition_B_Sample_Alpha_02.mzML
[81:02] 4880109 library precursors are potentially detectable
[81:05] 14138 spectra added to the library
[81:06] Loading run D:\Proteobench_manuscript_data\LFQ_Orbitrap_AIF_Condition_B_Sample_Alpha_03.mzML
[82:23] 4880109 library precursors are potentially detectable
[82:24] 6999 spectra added to the library
[82:26] Saving spectral library to D:\Proteobench_manuscript_data\run_output\diann_1.8_default\report-lib.tsv
[82:44] 107668 precursors saved
[82:44] Loading the generated library and saving it in the .speclib format
[82:44] Loading spectral library D:\Proteobench_manuscript_data\run_output\diann_1.8_default\report-lib.tsv
[82:51] Spectral library loaded: 12220 protein isoforms, 12545 protein groups and 107668 precursors in 94242 elution groups.
[82:51] Loading protein annotations from FASTA D:\Proteobench_manuscript_data\ProteoBenchFASTA_DDAQuantification.fasta
[82:52] Protein names missing for some isoforms
[82:52] Gene names missing for some isoforms
[82:52] Library contains 12167 proteins, and 0 genes
[82:52] Saving the library to D:\Proteobench_manuscript_data\run_output\diann_1.8_default\report-lib.tsv.speclib

[82:56] Second pass: using the newly created spectral library to reanalyse the data
[82:56] File #1/6
[82:56] Loading run D:\Proteobench_manuscript_data\LFQ_Orbitrap_AIF_Condition_A_Sample_Alpha_01.mzML
[84:02] 107668 library precursors are potentially detectable
[84:02] Processing...
[84:03] RT window set to 2.58557
[84:03] Recommended MS1 mass accuracy setting: 8.89384 ppm
[84:08] Removing low confidence identifications
[84:08] Searching PTM decoys
[84:08] Removing interfering precursors
[84:11] Training neural networks: 102783 targets, 74768 decoys
[84:16] Number of IDs at 0.01 FDR: 96952
[84:17] Calculating protein q-values
[84:17] Number of genes identified at 1% FDR: 0 (precursor-level), 0 (protein-level) (inference performed using proteotypic peptides only)
[84:17] Quantification
[84:18] Precursors with monitored PTMs at 1% FDR: 917 out of 917
[84:18] Unmodified precursors with monitored PTM sites at 1% FDR: 389 out of 389

[84:20] File #2/6
[84:20] Loading run D:\Proteobench_manuscript_data\LFQ_Orbitrap_AIF_Condition_A_Sample_Alpha_02.mzML
[85:28] 107668 library precursors are potentially detectable
[85:28] Processing...
[85:29] RT window set to 2.49374
[85:29] Recommended MS1 mass accuracy setting: 8.49241 ppm
[85:34] Removing low confidence identifications
[85:34] Searching PTM decoys
[85:34] Removing interfering precursors
[85:37] Training neural networks: 103099 targets, 78331 decoys
[85:43] Number of IDs at 0.01 FDR: 97767
[85:44] Calculating protein q-values
[85:44] Number of genes identified at 1% FDR: 0 (precursor-level), 0 (protein-level) (inference performed using proteotypic peptides only)
[85:44] Quantification
[85:45] Precursors with monitored PTMs at 1% FDR: 914 out of 914
[85:45] Unmodified precursors with monitored PTM sites at 1% FDR: 397 out of 397

[85:48] File #3/6
[85:48] Loading run D:\Proteobench_manuscript_data\LFQ_Orbitrap_AIF_Condition_A_Sample_Alpha_03.mzML
[86:55] 107668 library precursors are potentially detectable
[86:55] Processing...
[86:56] RT window set to 2.54534
[86:56] Recommended MS1 mass accuracy setting: 8.85537 ppm
[87:01] Removing low confidence identifications
[87:01] Searching PTM decoys
[87:01] Removing interfering precursors
[87:04] Training neural networks: 100018 targets, 70151 decoys
[87:09] Number of IDs at 0.01 FDR: 93644
[87:11] Calculating protein q-values
[87:11] Number of genes identified at 1% FDR: 0 (precursor-level), 0 (protein-level) (inference performed using proteotypic peptides only)
[87:11] Quantification
[87:11] Precursors with monitored PTMs at 1% FDR: 888 out of 888
[87:11] Unmodified precursors with monitored PTM sites at 1% FDR: 376 out of 376

[87:13] File #4/6
[87:13] Loading run D:\Proteobench_manuscript_data\LFQ_Orbitrap_AIF_Condition_B_Sample_Alpha_01.mzML
[88:20] 107668 library precursors are potentially detectable
[88:20] Processing...
[88:21] RT window set to 2.56681
[88:21] Recommended MS1 mass accuracy setting: 7.38678 ppm
[88:26] Removing low confidence identifications
[88:26] Searching PTM decoys
[88:26] Removing interfering precursors
[88:28] Training neural networks: 96904 targets, 72967 decoys
[88:33] Number of IDs at 0.01 FDR: 83811
[88:35] Calculating protein q-values
[88:35] Number of genes identified at 1% FDR: 0 (precursor-level), 0 (protein-level) (inference performed using proteotypic peptides only)
[88:35] Quantification
[88:35] Precursors with monitored PTMs at 1% FDR: 670 out of 695
[88:35] Unmodified precursors with monitored PTM sites at 1% FDR: 363 out of 371

[88:37] File #5/6
[88:37] Loading run D:\Proteobench_manuscript_data\LFQ_Orbitrap_AIF_Condition_B_Sample_Alpha_02.mzML
[89:44] 107668 library precursors are potentially detectable
[89:44] Processing...
[89:45] RT window set to 2.51776
[89:45] Recommended MS1 mass accuracy setting: 8.32546 ppm
[89:51] Removing low confidence identifications
[89:51] Searching PTM decoys
[89:51] Removing interfering precursors
[89:53] Training neural networks: 98477 targets, 77927 decoys
[89:59] Number of IDs at 0.01 FDR: 86147
[90:00] Calculating protein q-values
[90:00] Number of genes identified at 1% FDR: 0 (precursor-level), 0 (protein-level) (inference performed using proteotypic peptides only)
[90:00] Quantification
[90:01] Precursors with monitored PTMs at 1% FDR: 658 out of 690
[90:01] Unmodified precursors with monitored PTM sites at 1% FDR: 375 out of 392

[90:03] File #6/6
[90:03] Loading run D:\Proteobench_manuscript_data\LFQ_Orbitrap_AIF_Condition_B_Sample_Alpha_03.mzML
[91:10] 107668 library precursors are potentially detectable
[91:10] Processing...
[91:11] RT window set to 2.61627
[91:11] Recommended MS1 mass accuracy setting: 9.33614 ppm
[91:16] Removing low confidence identifications
[91:16] Searching PTM decoys
[91:16] Removing interfering precursors
[91:18] Training neural networks: 95671 targets, 69434 decoys
[91:24] Number of IDs at 0.01 FDR: 82602
[91:25] Calculating protein q-values
[91:25] Number of genes identified at 1% FDR: 0 (precursor-level), 0 (protein-level) (inference performed using proteotypic peptides only)
[91:25] Quantification
[91:25] Precursors with monitored PTMs at 1% FDR: 674 out of 702
[91:25] Unmodified precursors with monitored PTM sites at 1% FDR: 352 out of 367

[91:27] Cross-run analysis
[91:27] Reading quantification information: 6 files
[91:29] Quantifying peptides
[91:39] Quantifying proteins
[91:39] Calculating q-values for protein and gene groups
[91:40] Calculating global q-values for protein and gene groups
[91:41] Writing report
[92:15] Report saved to D:\Proteobench_manuscript_data\run_output\diann_1.8_default\report.tsv.
[92:15] Saving precursor levels matrix
[92:16] Precursor levels matrix (1% precursor and protein group FDR) saved to D:\Proteobench_manuscript_data\run_output\diann_1.8_default\report.pr_matrix.tsv.
[92:16] Saving protein group levels matrix
[92:16] Protein group levels matrix (1% precursor FDR and protein group FDR) saved to D:\Proteobench_manuscript_data\run_output\diann_1.8_default\report.pg_matrix.tsv.
[92:16] Saving gene group levels matrix
[92:16] Gene groups levels matrix (1% precursor FDR and protein group FDR) saved to D:\Proteobench_manuscript_data\run_output\diann_1.8_default\report.gg_matrix.tsv.
[92:16] Saving unique genes levels matrix
[92:16] Unique genes levels matrix (1% precursor FDR and protein group FDR) saved to D:\Proteobench_manuscript_data\run_output\diann_1.8_default\report.unique_genes_matrix.tsv.
[92:16] Stats report saved to D:\Proteobench_manuscript_data\run_output\diann_1.8_default\report.stats.tsv
