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 15:20:52 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_default/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 

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
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
[0:08] Processing FASTA
[0:18] Assembling elution groups
[0:28] 5116692 precursors generated
[0:28] Protein names missing for some isoforms
[0:28] Gene names missing for some isoforms
[0:28] Library contains 31685 proteins, and 0 genes
[0:37] [0:59] [3:46] [4:01] [4:08] [4:12] Saving the library to run_output_Astral/diann_1.9.2_default/report-lib.predicted.speclib
[4:21] Initialising library
[4:33] Loading spectral library run_output_Astral/diann_1.9.2_default/report-lib.predicted.speclib
[4:36] Library annotated with sequence database(s): ProteoBenchFASTA_DDAQuantification.fasta
[4:37] Spectral library loaded: 31837 protein isoforms, 51765 protein groups and 5116692 precursors in 2716663 elution groups.
[4:37] Loading protein annotations from FASTA ProteoBenchFASTA_DDAQuantification.fasta
[4:37] Annotating library proteins with information from the FASTA database
[4:37] Protein names missing for some isoforms
[4:37] Gene names missing for some isoforms
[4:37] Library contains 31685 proteins, and 0 genes
[4:42] Initialising library

First pass: generating a spectral library from DIA data

[4:59] File #1/6
[4:59] Loading run Raw_Astral/mzmls/LFQ_Astral_DIA_15min_50ng_Condition_A_REP1.mzML
[5:27] 5020855 library precursors are potentially detectable
[5:28] Calibrating with mass accuracies 30 (MS1), 20 (MS2)
[5:35] RT window set to 0.920269
[5:35] Peak width: 2.748
[5:35] Scan window radius set to 6
[5:35] Recommended MS1 mass accuracy setting: 2.42372 ppm
[5:49] Optimised mass accuracy: 8.32578 ppm
[6:14] Removing low confidence identifications
[6:25] Precursors at 1% peptidoform FDR: 63521
[6:26] Removing interfering precursors
[6:30] Training neural networks on 325187 PSMs
[6:36] Number of IDs at 0.01 FDR: 98678
[6:39] Precursors at 1% peptidoform FDR: 84635
[6:39] Calculating protein q-values
[6:39] Number of genes identified at 1% FDR: 0 (precursor-level), 0 (protein-level) (inference performed using proteotypic peptides only)
[6:39] Quantification
[6:40] Precursors with monitored PTMs at 1% FDR: 2478 out of 21529 considered
[6:40] Unmodified precursors with monitored PTM sites at 1% FDR: 15926
[6:40] Precursors with PTMs localised (when required) with > 90% confidence: 2414 out of 2478
[6:41] Quantification information saved to Raw_Astral/mzmls/LFQ_Astral_DIA_15min_50ng_Condition_A_REP1.mzML.quant

[6:41] File #2/6
[6:41] Loading run Raw_Astral/mzmls/LFQ_Astral_DIA_15min_50ng_Condition_A_REP2.mzML
[7:08] 5020855 library precursors are potentially detectable
[7:08] Calibrating with mass accuracies 30 (MS1), 18.8642 (MS2)
[7:14] RT window set to 0.836641
[7:14] Recommended MS1 mass accuracy setting: 2.46115 ppm
[7:36] Removing low confidence identifications
[7:47] Precursors at 1% peptidoform FDR: 63818
[7:47] Removing interfering precursors
[7:51] Training neural networks on 330279 PSMs
[7:58] Number of IDs at 0.01 FDR: 98508
[8:01] Precursors at 1% peptidoform FDR: 83981
[8:01] Calculating protein q-values
[8:01] Number of genes identified at 1% FDR: 0 (precursor-level), 0 (protein-level) (inference performed using proteotypic peptides only)
[8:01] Quantification
[8:02] Precursors with monitored PTMs at 1% FDR: 2423 out of 21411 considered
[8:02] Unmodified precursors with monitored PTM sites at 1% FDR: 15429
[8:02] Precursors with PTMs localised (when required) with > 90% confidence: 2370 out of 2423
[8:03] Quantification information saved to Raw_Astral/mzmls/LFQ_Astral_DIA_15min_50ng_Condition_A_REP2.mzML.quant

[8:03] File #3/6
[8:03] Loading run Raw_Astral/mzmls/LFQ_Astral_DIA_15min_50ng_Condition_A_REP3.mzML
[8:29] 5020855 library precursors are potentially detectable
[8:30] Calibrating with mass accuracies 30 (MS1), 18.2063 (MS2)
[8:36] RT window set to 1.05907
[8:36] Recommended MS1 mass accuracy setting: 2.55657 ppm
[9:02] Removing low confidence identifications
[9:14] Precursors at 1% peptidoform FDR: 64275
[9:15] Removing interfering precursors
[9:20] Training neural networks on 334863 PSMs
[9:27] Number of IDs at 0.01 FDR: 100344
[9:29] Precursors at 1% peptidoform FDR: 85881
[9:30] Calculating protein q-values
[9:30] Number of genes identified at 1% FDR: 0 (precursor-level), 0 (protein-level) (inference performed using proteotypic peptides only)
[9:30] Quantification
[9:31] Precursors with monitored PTMs at 1% FDR: 2336 out of 21751 considered
[9:31] Unmodified precursors with monitored PTM sites at 1% FDR: 15855
[9:31] Precursors with PTMs localised (when required) with > 90% confidence: 2278 out of 2336
[9:32] Quantification information saved to Raw_Astral/mzmls/LFQ_Astral_DIA_15min_50ng_Condition_A_REP3.mzML.quant

[9:32] File #4/6
[9:32] Loading run /home/robbe/ProteoBench_diaPASEF/Raw_Astral/mzmls/LFQ_Astral_DIA_15min_50ng_Condition_B_REP1.mzML
[9:58] 5020855 library precursors are potentially detectable
[9:58] Calibrating with mass accuracies 30 (MS1), 18.835 (MS2)
[10:04] RT window set to 1.00069
[10:04] Recommended MS1 mass accuracy setting: 2.49086 ppm
[10:30] Removing low confidence identifications
[10:42] Precursors at 1% peptidoform FDR: 65497
[10:43] Removing interfering precursors
[10:47] Training neural networks on 336440 PSMs
[10:58] Number of IDs at 0.01 FDR: 99510
[11:03] Precursors at 1% peptidoform FDR: 85479
[11:04] Calculating protein q-values
[11:04] Number of genes identified at 1% FDR: 0 (precursor-level), 0 (protein-level) (inference performed using proteotypic peptides only)
[11:04] Quantification
[11:05] Precursors with monitored PTMs at 1% FDR: 3023 out of 22835 considered
[11:05] Unmodified precursors with monitored PTM sites at 1% FDR: 16097
[11:05] Precursors with PTMs localised (when required) with > 90% confidence: 2955 out of 3023
[11:06] Quantification information saved to /home/robbe/ProteoBench_diaPASEF/Raw_Astral/mzmls/LFQ_Astral_DIA_15min_50ng_Condition_B_REP1.mzML.quant

[11:06] File #5/6
[11:06] Loading run /home/robbe/ProteoBench_diaPASEF/Raw_Astral/mzmls/LFQ_Astral_DIA_15min_50ng_Condition_B_REP2.mzML
[11:41] 5020855 library precursors are potentially detectable
[11:42] Calibrating with mass accuracies 30 (MS1), 19.0138 (MS2)
[11:54] RT window set to 0.825425
[11:54] Recommended MS1 mass accuracy setting: 2.21955 ppm
[12:22] Removing low confidence identifications
[12:36] Precursors at 1% peptidoform FDR: 63911
[12:37] Removing interfering precursors
[12:44] Training neural networks on 332410 PSMs
[12:54] Number of IDs at 0.01 FDR: 98742
[12:58] Precursors at 1% peptidoform FDR: 84214
[12:58] Calculating protein q-values
[12:59] Number of genes identified at 1% FDR: 0 (precursor-level), 0 (protein-level) (inference performed using proteotypic peptides only)
[12:59] Quantification
[12:59] Precursors with monitored PTMs at 1% FDR: 2789 out of 22097 considered
[12:59] Unmodified precursors with monitored PTM sites at 1% FDR: 16051
[12:59] Precursors with PTMs localised (when required) with > 90% confidence: 2723 out of 2789
[13:00] Quantification information saved to /home/robbe/ProteoBench_diaPASEF/Raw_Astral/mzmls/LFQ_Astral_DIA_15min_50ng_Condition_B_REP2.mzML.quant

[13:00] File #6/6
[13:00] Loading run /home/robbe/ProteoBench_diaPASEF/Raw_Astral/mzmls/LFQ_Astral_DIA_15min_50ng_Condition_B_REP3.mzML
[13:35] 5020855 library precursors are potentially detectable
[13:36] Calibrating with mass accuracies 30 (MS1), 18.9828 (MS2)
[13:46] RT window set to 0.887665
[13:46] Recommended MS1 mass accuracy setting: 2.51973 ppm
[14:17] Removing low confidence identifications
[14:31] Precursors at 1% peptidoform FDR: 64414
[14:32] Removing interfering precursors
[14:39] Training neural networks on 333740 PSMs
[14:50] Number of IDs at 0.01 FDR: 99239
[14:54] Precursors at 1% peptidoform FDR: 84075
[14:55] Calculating protein q-values
[14:55] Number of genes identified at 1% FDR: 0 (precursor-level), 0 (protein-level) (inference performed using proteotypic peptides only)
[14:56] Quantification
[14:56] Precursors with monitored PTMs at 1% FDR: 2941 out of 22731 considered
[14:56] Unmodified precursors with monitored PTM sites at 1% FDR: 15777
[14:56] Precursors with PTMs localised (when required) with > 90% confidence: 2855 out of 2941
[14:57] Quantification information saved to /home/robbe/ProteoBench_diaPASEF/Raw_Astral/mzmls/LFQ_Astral_DIA_15min_50ng_Condition_B_REP3.mzML.quant

[14:57] Cross-run analysis
[14:57] Reading quantification information: 6 files
[15:05] Quantifying peptides
[15:48] Assembling protein groups
[15:51] Quantifying proteins
[15:52] Calculating q-values for protein and gene groups
[15:53] Calculating global q-values for protein and gene groups
[15:54] Protein groups with global q-value <= 0.01: 11302
[15:58] Compressed report saved to run_output_Astral/diann_1.9.2_default/report-first-pass.parquet. Use R 'arrow' or Python 'PyArrow' package to process
[15:58] Writing report
[16:14] Report saved to run_output_Astral/diann_1.9.2_default/report-first-pass.tsv.
[16:14] Stats report saved to run_output_Astral/diann_1.9.2_default/report-first-pass.stats.tsv
[16:14] Generating spectral library:
[16:17] 128024 target and 1302 decoy precursors saved
[16:17] Spectral library saved to run_output_Astral/diann_1.9.2_default/report-lib.parquet

[16:18] Loading spectral library run_output_Astral/diann_1.9.2_default/report-lib.parquet
[16:20] Spectral library loaded: 13271 protein isoforms, 13081 protein groups and 129326 precursors in 121239 elution groups.
[16:20] Loading protein annotations from FASTA ProteoBenchFASTA_DDAQuantification.fasta
[16:20] Annotating library proteins with information from the FASTA database
[16:20] Gene names missing for some isoforms
[16:20] Library contains 13260 proteins, and 0 genes
[16:21] Initialising library
[16:21] Saving the library to run_output_Astral/diann_1.9.2_default/report-lib.parquet.skyline.speclib


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

[16:21] File #1/6
[16:21] Loading run Raw_Astral/mzmls/LFQ_Astral_DIA_15min_50ng_Condition_A_REP1.mzML
[16:55] 128024 library precursors are potentially detectable
[16:55] Calibrating with mass accuracies 30 (MS1), 18.1455 (MS2)
[16:56] RT window set to 0.368442
[16:56] Recommended MS1 mass accuracy setting: 2.81263 ppm
[16:58] Removing low confidence identifications
[17:01] Precursors at 1% peptidoform FDR: 75527
[17:01] Removing interfering precursors
[17:02] Training neural networks on 170459 PSMs
[17:07] Number of IDs at 0.01 FDR: 108717
[17:09] Precursors at 1% peptidoform FDR: 94648
[17:09] Calculating protein q-values
[17:09] Number of genes identified at 1% FDR: 0 (precursor-level), 0 (protein-level) (inference performed using proteotypic peptides only)
[17:09] Quantification
[17:10] Precursors with monitored PTMs at 1% FDR: 3071 out of 23968 considered
[17:10] Unmodified precursors with monitored PTM sites at 1% FDR: 17722
[17:10] Precursors with PTMs localised (when required) with > 90% confidence: 2993 out of 3071

[17:10] File #2/6
[17:10] Loading run Raw_Astral/mzmls/LFQ_Astral_DIA_15min_50ng_Condition_A_REP2.mzML
[17:42] 128024 library precursors are potentially detectable
[17:42] Calibrating with mass accuracies 30 (MS1), 18.497 (MS2)
[17:43] RT window set to 0.42101
[17:43] Recommended MS1 mass accuracy setting: 2.52879 ppm
[17:45] Removing low confidence identifications
[17:48] Precursors at 1% peptidoform FDR: 72970
[17:48] Removing interfering precursors
[17:49] Training neural networks on 171021 PSMs
[17:53] Number of IDs at 0.01 FDR: 109418
[17:57] Precursors at 1% peptidoform FDR: 94846
[17:57] Calculating protein q-values
[17:57] Number of genes identified at 1% FDR: 0 (precursor-level), 0 (protein-level) (inference performed using proteotypic peptides only)
[17:57] Quantification
[17:57] Precursors with monitored PTMs at 1% FDR: 3090 out of 23877 considered
[17:57] Unmodified precursors with monitored PTM sites at 1% FDR: 17726
[17:57] Precursors with PTMs localised (when required) with > 90% confidence: 3028 out of 3090

[17:57] File #3/6
[17:57] Loading run Raw_Astral/mzmls/LFQ_Astral_DIA_15min_50ng_Condition_A_REP3.mzML
[18:31] 128024 library precursors are potentially detectable
[18:31] Calibrating with mass accuracies 30 (MS1), 17.8652 (MS2)
[18:32] RT window set to 0.431706
[18:32] Recommended MS1 mass accuracy setting: 2.59752 ppm
[18:34] Removing low confidence identifications
[18:37] Precursors at 1% peptidoform FDR: 76686
[18:38] Removing interfering precursors
[18:39] Training neural networks on 171068 PSMs
[18:44] Number of IDs at 0.01 FDR: 109481
[18:47] Precursors at 1% peptidoform FDR: 95380
[18:47] Calculating protein q-values
[18:47] Number of genes identified at 1% FDR: 0 (precursor-level), 0 (protein-level) (inference performed using proteotypic peptides only)
[18:47] Quantification
[18:47] Precursors with monitored PTMs at 1% FDR: 3102 out of 23991 considered
[18:47] Unmodified precursors with monitored PTM sites at 1% FDR: 17803
[18:47] Precursors with PTMs localised (when required) with > 90% confidence: 3033 out of 3102

[18:47] File #4/6
[18:47] Loading run /home/robbe/ProteoBench_diaPASEF/Raw_Astral/mzmls/LFQ_Astral_DIA_15min_50ng_Condition_B_REP1.mzML
[19:23] 128024 library precursors are potentially detectable
[19:23] Calibrating with mass accuracies 30 (MS1), 18.5173 (MS2)
[19:24] RT window set to 0.411008
[19:24] Recommended MS1 mass accuracy setting: 2.8673 ppm
[19:26] Removing low confidence identifications
[19:29] Precursors at 1% peptidoform FDR: 78665
[19:29] Removing interfering precursors
[19:31] Training neural networks on 171451 PSMs
[19:36] Number of IDs at 0.01 FDR: 109654
[19:39] Precursors at 1% peptidoform FDR: 96146
[19:39] Calculating protein q-values
[19:39] Number of genes identified at 1% FDR: 0 (precursor-level), 0 (protein-level) (inference performed using proteotypic peptides only)
[19:39] Quantification
[19:39] Precursors with monitored PTMs at 1% FDR: 3287 out of 24453 considered
[19:39] Unmodified precursors with monitored PTM sites at 1% FDR: 18119
[19:39] Precursors with PTMs localised (when required) with > 90% confidence: 3220 out of 3287

[19:40] File #5/6
[19:40] Loading run /home/robbe/ProteoBench_diaPASEF/Raw_Astral/mzmls/LFQ_Astral_DIA_15min_50ng_Condition_B_REP2.mzML
[20:16] 128024 library precursors are potentially detectable
[20:16] Calibrating with mass accuracies 30 (MS1), 18.6559 (MS2)
[20:17] RT window set to 0.43201
[20:17] Recommended MS1 mass accuracy setting: 2.42903 ppm
[20:19] Removing low confidence identifications
[20:22] Precursors at 1% peptidoform FDR: 77557
[20:23] Removing interfering precursors
[20:24] Training neural networks on 171641 PSMs
[20:28] Number of IDs at 0.01 FDR: 110017
[20:32] Precursors at 1% peptidoform FDR: 95728
[20:32] Calculating protein q-values
[20:32] Number of genes identified at 1% FDR: 0 (precursor-level), 0 (protein-level) (inference performed using proteotypic peptides only)
[20:32] Quantification
[20:32] Precursors with monitored PTMs at 1% FDR: 3277 out of 24248 considered
[20:32] Unmodified precursors with monitored PTM sites at 1% FDR: 17984
[20:32] Precursors with PTMs localised (when required) with > 90% confidence: 3215 out of 3277

[20:33] File #6/6
[20:33] Loading run /home/robbe/ProteoBench_diaPASEF/Raw_Astral/mzmls/LFQ_Astral_DIA_15min_50ng_Condition_B_REP3.mzML
[21:08] 128024 library precursors are potentially detectable
[21:08] Calibrating with mass accuracies 30 (MS1), 18.8274 (MS2)
[21:09] RT window set to 0.431722
[21:09] Recommended MS1 mass accuracy setting: 3.01989 ppm
[21:10] Removing low confidence identifications
[21:14] Precursors at 1% peptidoform FDR: 75822
[21:14] Removing interfering precursors
[21:15] Training neural networks on 171574 PSMs
[21:19] Number of IDs at 0.01 FDR: 109657
[21:22] Precursors at 1% peptidoform FDR: 96198
[21:22] Calculating protein q-values
[21:22] Number of genes identified at 1% FDR: 0 (precursor-level), 0 (protein-level) (inference performed using proteotypic peptides only)
[21:22] Quantification
[21:22] Precursors with monitored PTMs at 1% FDR: 3293 out of 24513 considered
[21:22] Unmodified precursors with monitored PTM sites at 1% FDR: 18128
[21:22] Precursors with PTMs localised (when required) with > 90% confidence: 3231 out of 3293

[21:23] Cross-run analysis
[21:23] Reading quantification information: 6 files
[21:25] Quantifying peptides
[22:51] Quantification parameters: 0.348656, 0.00146821, 0.00151654, 0.0122075, 0.012026, 0.012093, 0.194941, 0.232606, 0.181498, 0.0136356, 0.0361166, 0.0144396, 0.387251, 0.0526839, 0.0763135, 0.0117969
[23:11] Quantifying proteins
[23:11] Calculating q-values for protein and gene groups
[23:11] Calculating global q-values for protein and gene groups
[23:11] Protein groups with global q-value <= 0.01: 10999
[23:16] Compressed report saved to run_output_Astral/diann_1.9.2_default/report.parquet. Use R 'arrow' or Python 'PyArrow' package to process
[23:16] Writing report
[23:34] Report saved to run_output_Astral/diann_1.9.2_default/report.tsv.
[23:34] Stats report saved to run_output_Astral/diann_1.9.2_default/report.stats.tsv

