DIA-NN 1.9.2 (Data-Independent Acquisition by Neural Networks)
Compiled on Oct 31 2024 04:27:44
Current date and time: Wed Apr 16 16:14:39 2025
Logical CPU cores: 128
diann-1.9.2/diann-linux --f Raw_Astral/mzmls/LFQ_Astral_DIA_15min_50ng_Condition_A_REP1.mzML --f Raw_Astral/mzmls/LFQ_Astral_DIA_15min_50ng_Condition_A_REP2.mzML --f Raw_Astral/mzmls/LFQ_Astral_DIA_15min_50ng_Condition_A_REP3.mzML --f /home/robbe/ProteoBench_diaPASEF/Raw_Astral/mzmls/LFQ_Astral_DIA_15min_50ng_Condition_B_REP1.mzML --f /home/robbe/ProteoBench_diaPASEF/Raw_Astral/mzmls/LFQ_Astral_DIA_15min_50ng_Condition_B_REP2.mzML --f /home/robbe/ProteoBench_diaPASEF/Raw_Astral/mzmls/LFQ_Astral_DIA_15min_50ng_Condition_B_REP3.mzML --lib --threads 80 --verbose 1 --out run_output_Astral/diann_1.9.2_conservativenn/report.tsv --qvalue 0.01 --gen-spec-lib --predictor --fasta 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 --conservative 

Thread number set to 80
Output will be filtered at 0.01 FDR
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
Conservative machine learning mode enabled
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 ProteoBenchFASTA_DDAQuantification.fasta
[0:09] Processing FASTA
[0:16] Assembling elution groups
[0:25] 5116692 precursors generated
[0:25] Protein names missing for some isoforms
[0:25] Gene names missing for some isoforms
[0:25] Library contains 31685 proteins, and 0 genes
[0:32] [0:52] [2:59] [3:16] [3:29] [3:35] Saving the library to run_output_Astral/diann_1.9.2_conservativenn/report-lib.predicted.speclib
[3:51] Initialising library
[4:08] Loading spectral library run_output_Astral/diann_1.9.2_conservativenn/report-lib.predicted.speclib
[4:12] Library annotated with sequence database(s): ProteoBenchFASTA_DDAQuantification.fasta
[4:13] Spectral library loaded: 31837 protein isoforms, 51765 protein groups and 5116692 precursors in 2716663 elution groups.
[4:13] Loading protein annotations from FASTA ProteoBenchFASTA_DDAQuantification.fasta
[4:14] Annotating library proteins with information from the FASTA database
[4:14] Protein names missing for some isoforms
[4:14] Gene names missing for some isoforms
[4:14] Library contains 31685 proteins, and 0 genes
[4:21] Initialising library

First pass: generating a spectral library from DIA data

[4:42] File #1/6
[4:42] Loading run Raw_Astral/mzmls/LFQ_Astral_DIA_15min_50ng_Condition_A_REP1.mzML
[5:23] 5020855 library precursors are potentially detectable
[5:24] Calibrating with mass accuracies 30 (MS1), 20 (MS2)
[5:35] RT window set to 1.07054
[5:35] Peak width: 2.788
[5:35] Scan window radius set to 6
[5:35] Recommended MS1 mass accuracy setting: 2.39031 ppm
[5:55] Optimised mass accuracy: 8.37286 ppm
[6:35] Removing low confidence identifications
[6:53] Precursors at 1% peptidoform FDR: 64208
[6:53] Removing interfering precursors
[7:02] Training neural networks on 329465 PSMs
[7:13] Number of IDs at 0.01 FDR: 96836
[7:19] Precursors at 1% peptidoform FDR: 93484
[7:20] Calculating protein q-values
[7:20] Number of genes identified at 1% FDR: 0 (precursor-level), 0 (protein-level) (inference performed using proteotypic peptides only)
[7:21] Quantification
[7:21] Precursors with monitored PTMs at 1% FDR: 2578 out of 21012 considered
[7:21] Unmodified precursors with monitored PTM sites at 1% FDR: 17397
[7:21] Precursors with PTMs localised (when required) with > 90% confidence: 2513 out of 2578
[7:23] Quantification information saved to Raw_Astral/mzmls/LFQ_Astral_DIA_15min_50ng_Condition_A_REP1.mzML.quant

[7:23] File #2/6
[7:23] Loading run Raw_Astral/mzmls/LFQ_Astral_DIA_15min_50ng_Condition_A_REP2.mzML
[8:03] 5020855 library precursors are potentially detectable
[8:04] Calibrating with mass accuracies 30 (MS1), 18.8642 (MS2)
[8:15] RT window set to 0.87885
[8:15] Recommended MS1 mass accuracy setting: 2.45865 ppm
[8:44] Removing low confidence identifications
[9:01] Precursors at 1% peptidoform FDR: 64307
[9:02] Removing interfering precursors
[9:09] Training neural networks on 333453 PSMs
[9:21] Number of IDs at 0.01 FDR: 96018
[9:26] Precursors at 1% peptidoform FDR: 91363
[9:27] Calculating protein q-values
[9:27] Number of genes identified at 1% FDR: 0 (precursor-level), 0 (protein-level) (inference performed using proteotypic peptides only)
[9:27] Quantification
[9:28] Precursors with monitored PTMs at 1% FDR: 2386 out of 21206 considered
[9:28] Unmodified precursors with monitored PTM sites at 1% FDR: 17076
[9:28] Precursors with PTMs localised (when required) with > 90% confidence: 2324 out of 2386
[9:29] Quantification information saved to Raw_Astral/mzmls/LFQ_Astral_DIA_15min_50ng_Condition_A_REP2.mzML.quant

[9:29] File #3/6
[9:29] Loading run Raw_Astral/mzmls/LFQ_Astral_DIA_15min_50ng_Condition_A_REP3.mzML
[10:16] 5020855 library precursors are potentially detectable
[10:16] Calibrating with mass accuracies 30 (MS1), 18.2063 (MS2)
[10:24] RT window set to 1.13804
[10:24] Recommended MS1 mass accuracy setting: 2.54696 ppm
[11:04] Removing low confidence identifications
[11:18] Precursors at 1% peptidoform FDR: 64892
[11:18] Removing interfering precursors
[11:24] Training neural networks on 336077 PSMs
[11:35] Number of IDs at 0.01 FDR: 96709
[11:41] Precursors at 1% peptidoform FDR: 93238
[11:41] Calculating protein q-values
[11:42] Number of genes identified at 1% FDR: 0 (precursor-level), 0 (protein-level) (inference performed using proteotypic peptides only)
[11:42] Quantification
[11:42] Precursors with monitored PTMs at 1% FDR: 2608 out of 21146 considered
[11:42] Unmodified precursors with monitored PTM sites at 1% FDR: 17534
[11:43] Precursors with PTMs localised (when required) with > 90% confidence: 2526 out of 2608
[11:44] Quantification information saved to Raw_Astral/mzmls/LFQ_Astral_DIA_15min_50ng_Condition_A_REP3.mzML.quant

[11:44] File #4/6
[11:44] Loading run /home/robbe/ProteoBench_diaPASEF/Raw_Astral/mzmls/LFQ_Astral_DIA_15min_50ng_Condition_B_REP1.mzML
[12:29] 5020855 library precursors are potentially detectable
[12:29] Calibrating with mass accuracies 30 (MS1), 18.835 (MS2)
[12:38] RT window set to 1.05606
[12:38] Recommended MS1 mass accuracy setting: 2.55778 ppm
[13:18] Removing low confidence identifications
[13:35] Precursors at 1% peptidoform FDR: 65254
[13:35] Removing interfering precursors
[13:41] Training neural networks on 335715 PSMs
[13:48] Number of IDs at 0.01 FDR: 96307
[13:50] Precursors at 1% peptidoform FDR: 92940
[13:51] Calculating protein q-values
[13:51] Number of genes identified at 1% FDR: 0 (precursor-level), 0 (protein-level) (inference performed using proteotypic peptides only)
[13:51] Quantification
[13:52] Precursors with monitored PTMs at 1% FDR: 3257 out of 22095 considered
[13:52] Unmodified precursors with monitored PTM sites at 1% FDR: 17432
[13:52] Precursors with PTMs localised (when required) with > 90% confidence: 3179 out of 3257
[13:53] Quantification information saved to /home/robbe/ProteoBench_diaPASEF/Raw_Astral/mzmls/LFQ_Astral_DIA_15min_50ng_Condition_B_REP1.mzML.quant

[13:53] File #5/6
[13:53] Loading run /home/robbe/ProteoBench_diaPASEF/Raw_Astral/mzmls/LFQ_Astral_DIA_15min_50ng_Condition_B_REP2.mzML
[14:29] 5020855 library precursors are potentially detectable
[14:30] Calibrating with mass accuracies 30 (MS1), 19.0138 (MS2)
[14:43] RT window set to 0.865453
[14:43] Recommended MS1 mass accuracy setting: 2.09982 ppm
[15:17] Removing low confidence identifications
[15:38] Precursors at 1% peptidoform FDR: 66194
[15:39] Removing interfering precursors
[15:45] Training neural networks on 333799 PSMs
[15:56] Number of IDs at 0.01 FDR: 96106
[16:00] Precursors at 1% peptidoform FDR: 93225
[16:01] Calculating protein q-values
[16:01] Number of genes identified at 1% FDR: 0 (precursor-level), 0 (protein-level) (inference performed using proteotypic peptides only)
[16:01] Quantification
[16:01] Precursors with monitored PTMs at 1% FDR: 3037 out of 21734 considered
[16:01] Unmodified precursors with monitored PTM sites at 1% FDR: 17736
[16:02] Precursors with PTMs localised (when required) with > 90% confidence: 2956 out of 3037
[16:03] Quantification information saved to /home/robbe/ProteoBench_diaPASEF/Raw_Astral/mzmls/LFQ_Astral_DIA_15min_50ng_Condition_B_REP2.mzML.quant

[16:03] File #6/6
[16:03] Loading run /home/robbe/ProteoBench_diaPASEF/Raw_Astral/mzmls/LFQ_Astral_DIA_15min_50ng_Condition_B_REP3.mzML
[16:56] 5020855 library precursors are potentially detectable
[16:57] Calibrating with mass accuracies 30 (MS1), 18.9828 (MS2)
[17:08] RT window set to 0.946856
[17:08] Recommended MS1 mass accuracy setting: 2.51481 ppm
[17:48] Removing low confidence identifications
[18:08] Precursors at 1% peptidoform FDR: 64925
[18:09] Removing interfering precursors
[18:17] Training neural networks on 334344 PSMs
[18:28] Number of IDs at 0.01 FDR: 96742
[18:32] Precursors at 1% peptidoform FDR: 93174
[18:32] Calculating protein q-values
[18:33] Number of genes identified at 1% FDR: 0 (precursor-level), 0 (protein-level) (inference performed using proteotypic peptides only)
[18:33] Quantification
[18:33] Precursors with monitored PTMs at 1% FDR: 2958 out of 21989 considered
[18:33] Unmodified precursors with monitored PTM sites at 1% FDR: 17517
[18:33] Precursors with PTMs localised (when required) with > 90% confidence: 2868 out of 2958
[18:35] Quantification information saved to /home/robbe/ProteoBench_diaPASEF/Raw_Astral/mzmls/LFQ_Astral_DIA_15min_50ng_Condition_B_REP3.mzML.quant

[18:35] Cross-run analysis
[18:35] Reading quantification information: 6 files
[18:42] Quantifying peptides
[19:23] Assembling protein groups
[19:26] Quantifying proteins
[19:27] Calculating q-values for protein and gene groups
[19:28] Calculating global q-values for protein and gene groups
[19:29] Protein groups with global q-value <= 0.01: 11136
[19:32] Compressed report saved to run_output_Astral/diann_1.9.2_conservativenn/report-first-pass.parquet. Use R 'arrow' or Python 'PyArrow' package to process
[19:32] Writing report
[19:45] Report saved to run_output_Astral/diann_1.9.2_conservativenn/report-first-pass.tsv.
[19:45] Stats report saved to run_output_Astral/diann_1.9.2_conservativenn/report-first-pass.stats.tsv
[19:45] Generating spectral library:
[19:48] 123562 target and 1264 decoy precursors saved
[19:48] Spectral library saved to run_output_Astral/diann_1.9.2_conservativenn/report-lib.parquet

[19:49] Loading spectral library run_output_Astral/diann_1.9.2_conservativenn/report-lib.parquet
[19:51] Spectral library loaded: 12991 protein isoforms, 12812 protein groups and 124826 precursors in 117192 elution groups.
[19:51] Loading protein annotations from FASTA ProteoBenchFASTA_DDAQuantification.fasta
[19:51] Annotating library proteins with information from the FASTA database
[19:51] Gene names missing for some isoforms
[19:51] Library contains 12980 proteins, and 0 genes
[19:51] Initialising library
[19:52] Saving the library to run_output_Astral/diann_1.9.2_conservativenn/report-lib.parquet.skyline.speclib


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

[19:52] File #1/6
[19:52] Loading run Raw_Astral/mzmls/LFQ_Astral_DIA_15min_50ng_Condition_A_REP1.mzML
[20:42] 123562 library precursors are potentially detectable
[20:42] Calibrating with mass accuracies 30 (MS1), 18.1459 (MS2)
[20:43] RT window set to 0.433784
[20:43] Recommended MS1 mass accuracy setting: 2.69036 ppm
[20:46] Removing low confidence identifications
[20:49] Precursors at 1% peptidoform FDR: 75240
[20:49] Removing interfering precursors
[20:50] Training neural networks on 165902 PSMs
[20:55] Number of IDs at 0.01 FDR: 103785
[20:58] Precursors at 1% peptidoform FDR: 92852
[20:58] Calculating protein q-values
[20:58] Number of genes identified at 1% FDR: 0 (precursor-level), 0 (protein-level) (inference performed using proteotypic peptides only)
[20:58] Quantification
[20:59] Precursors with monitored PTMs at 1% FDR: 2797 out of 22567 considered
[20:59] Unmodified precursors with monitored PTM sites at 1% FDR: 17400
[20:59] Precursors with PTMs localised (when required) with > 90% confidence: 2729 out of 2797

[20:59] File #2/6
[20:59] Loading run Raw_Astral/mzmls/LFQ_Astral_DIA_15min_50ng_Condition_A_REP2.mzML
[21:47] 123562 library precursors are potentially detectable
[21:48] Calibrating with mass accuracies 30 (MS1), 18.4918 (MS2)
[21:48] RT window set to 0.43759
[21:48] Recommended MS1 mass accuracy setting: 2.64344 ppm
[21:50] Removing low confidence identifications
[21:54] Precursors at 1% peptidoform FDR: 74000
[21:54] Removing interfering precursors
[21:55] Training neural networks on 166122 PSMs
[22:00] Number of IDs at 0.01 FDR: 104344
[22:03] Precursors at 1% peptidoform FDR: 92926
[22:03] Calculating protein q-values
[22:03] Number of genes identified at 1% FDR: 0 (precursor-level), 0 (protein-level) (inference performed using proteotypic peptides only)
[22:03] Quantification
[22:03] Precursors with monitored PTMs at 1% FDR: 2808 out of 22613 considered
[22:03] Unmodified precursors with monitored PTM sites at 1% FDR: 17314
[22:03] Precursors with PTMs localised (when required) with > 90% confidence: 2753 out of 2808

[22:04] File #3/6
[22:04] Loading run Raw_Astral/mzmls/LFQ_Astral_DIA_15min_50ng_Condition_A_REP3.mzML
[22:55] 123562 library precursors are potentially detectable
[22:55] Calibrating with mass accuracies 30 (MS1), 17.852 (MS2)
[22:56] RT window set to 0.432187
[22:56] Recommended MS1 mass accuracy setting: 2.59659 ppm
[22:58] Removing low confidence identifications
[23:02] Precursors at 1% peptidoform FDR: 74343
[23:03] Removing interfering precursors
[23:04] Training neural networks on 165943 PSMs
[23:10] Number of IDs at 0.01 FDR: 103780
[23:15] Precursors at 1% peptidoform FDR: 92548
[23:15] Calculating protein q-values
[23:15] Number of genes identified at 1% FDR: 0 (precursor-level), 0 (protein-level) (inference performed using proteotypic peptides only)
[23:15] Quantification
[23:15] Precursors with monitored PTMs at 1% FDR: 2803 out of 22572 considered
[23:15] Unmodified precursors with monitored PTM sites at 1% FDR: 17304
[23:15] Precursors with PTMs localised (when required) with > 90% confidence: 2744 out of 2803

[23:15] File #4/6
[23:15] Loading run /home/robbe/ProteoBench_diaPASEF/Raw_Astral/mzmls/LFQ_Astral_DIA_15min_50ng_Condition_B_REP1.mzML
[24:05] 123562 library precursors are potentially detectable
[24:05] Calibrating with mass accuracies 30 (MS1), 18.5263 (MS2)
[24:06] RT window set to 0.445991
[24:06] Recommended MS1 mass accuracy setting: 2.72261 ppm
[24:09] Removing low confidence identifications
[24:13] Precursors at 1% peptidoform FDR: 76327
[24:14] Removing interfering precursors
[24:15] Training neural networks on 166648 PSMs
[24:20] Number of IDs at 0.01 FDR: 105258
[24:24] Precursors at 1% peptidoform FDR: 94271
[24:24] Calculating protein q-values
[24:24] Number of genes identified at 1% FDR: 0 (precursor-level), 0 (protein-level) (inference performed using proteotypic peptides only)
[24:24] Quantification
[24:25] Precursors with monitored PTMs at 1% FDR: 2957 out of 23268 considered
[24:25] Unmodified precursors with monitored PTM sites at 1% FDR: 17784
[24:25] Precursors with PTMs localised (when required) with > 90% confidence: 2904 out of 2957

[24:25] File #5/6
[24:25] Loading run /home/robbe/ProteoBench_diaPASEF/Raw_Astral/mzmls/LFQ_Astral_DIA_15min_50ng_Condition_B_REP2.mzML
[25:16] 123562 library precursors are potentially detectable
[25:16] Calibrating with mass accuracies 30 (MS1), 18.6643 (MS2)
[25:17] RT window set to 0.44637
[25:17] Recommended MS1 mass accuracy setting: 2.64877 ppm
[25:19] Removing low confidence identifications
[25:22] Precursors at 1% peptidoform FDR: 75981
[25:23] Removing interfering precursors
[25:23] Training neural networks on 166314 PSMs
[25:28] Number of IDs at 0.01 FDR: 104704
[25:31] Precursors at 1% peptidoform FDR: 93830
[25:31] Calculating protein q-values
[25:31] Number of genes identified at 1% FDR: 0 (precursor-level), 0 (protein-level) (inference performed using proteotypic peptides only)
[25:31] Quantification
[25:32] Precursors with monitored PTMs at 1% FDR: 2927 out of 22944 considered
[25:32] Unmodified precursors with monitored PTM sites at 1% FDR: 17569
[25:32] Precursors with PTMs localised (when required) with > 90% confidence: 2867 out of 2927

[25:32] File #6/6
[25:32] Loading run /home/robbe/ProteoBench_diaPASEF/Raw_Astral/mzmls/LFQ_Astral_DIA_15min_50ng_Condition_B_REP3.mzML
[26:20] 123562 library precursors are potentially detectable
[26:20] Calibrating with mass accuracies 30 (MS1), 18.8307 (MS2)
[26:20] RT window set to 0.446284
[26:20] Recommended MS1 mass accuracy setting: 2.5897 ppm
[26:23] Removing low confidence identifications
[26:27] Precursors at 1% peptidoform FDR: 77434
[26:28] Removing interfering precursors
[26:29] Training neural networks on 166940 PSMs
[26:34] Number of IDs at 0.01 FDR: 104700
[26:38] Precursors at 1% peptidoform FDR: 93930
[26:38] Calculating protein q-values
[26:38] Number of genes identified at 1% FDR: 0 (precursor-level), 0 (protein-level) (inference performed using proteotypic peptides only)
[26:38] Quantification
[26:39] Precursors with monitored PTMs at 1% FDR: 2902 out of 23050 considered
[26:39] Unmodified precursors with monitored PTM sites at 1% FDR: 17679
[26:39] Precursors with PTMs localised (when required) with > 90% confidence: 2838 out of 2902

[26:39] Cross-run analysis
[26:39] Reading quantification information: 6 files
[26:41] Quantifying peptides
[27:49] Quantification parameters: 0.345761, 0.00147746, 0.0014982, 0.0121376, 0.0120397, 0.0120983, 0.195481, 0.223052, 0.178548, 0.0135907, 0.0372057, 0.0156786, 0.367423, 0.0524249, 0.0745369, 0.0114096
[28:06] Quantifying proteins
[28:06] Calculating q-values for protein and gene groups
[28:06] Calculating global q-values for protein and gene groups
[28:06] Protein groups with global q-value <= 0.01: 10690
[28:11] Compressed report saved to run_output_Astral/diann_1.9.2_conservativenn/report.parquet. Use R 'arrow' or Python 'PyArrow' package to process
[28:11] Writing report
[28:30] Report saved to run_output_Astral/diann_1.9.2_conservativenn/report.tsv.
[28:30] Stats report saved to run_output_Astral/diann_1.9.2_conservativenn/report.stats.tsv

