DIA-NN 1.9.2 (Data-Independent Acquisition by Neural Networks)
Compiled on Oct 20 2024 02:59:53
Current date and time: Tue Aug  4 14:24:34 2026
Logical CPU cores: 128
/usr/diann-1.9.2/diann --f /public/local/ProteoBench/HYE_Astral_mzml/LFQ_Astral_DIA_15min_50ng_Condition_A_REP1.mzML --f /public/local/ProteoBench/HYE_Astral_mzml/LFQ_Astral_DIA_15min_50ng_Condition_A_REP2.mzML --f /public/local/ProteoBench/HYE_Astral_mzml/LFQ_Astral_DIA_15min_50ng_Condition_A_REP3.mzML --f /public/local/ProteoBench/HYE_Astral_mzml/LFQ_Astral_DIA_15min_50ng_Condition_B_REP1.mzML --f /public/local/ProteoBench/HYE_Astral_mzml/LFQ_Astral_DIA_15min_50ng_Condition_B_REP2.mzML --f /public/local/ProteoBench/HYE_Astral_mzml/LFQ_Astral_DIA_15min_50ng_Condition_B_REP3.mzML --fasta /public/local/ProteoBench/fastas/ProteoBenchFASTA_MixedSpecies_HYE.fasta --out /home/robbe/PB_output/results/Astral_DIANN_version_comparison/HYE_Astral/diann_v1.9.2/report.tsv --temp /home/robbe/PB_output/results/Astral_DIANN_version_comparison/HYE_Astral/diann_v1.9.2 --threads 100 --missed-cleavages 1 --min-pep-len 6 --max-pep-len 30 --qvalue 0.01 --protein-qvalue 0.01 --min-pr-charge 1 --max-pr-charge 5 --min-pr-mz 380 --max-pr-mz 980 --min-fr-mz 150 --max-fr-mz 2000 --cut K*,R* --gen-spec-lib --predictor --unimod4 --var-mod UniMod:35,15.994915,M --var-mods 1 --gen-spec-lib --fasta-search --reanalyse 

Thread number set to 100
Maximum number of missed cleavages set to 1
Min peptide length set to 6
Max peptide length set to 30
Output will be filtered at 0.01 FDR
Output will be filtered at 0.01 protein-level FDR
Min precursor charge set to 1
Max precursor charge set to 5
Min precursor m/z set to 380
Max precursor m/z set to 980
Min fragment m/z set to 150
Max fragment m/z set to 2000
In silico digest will involve cuts at K*,R*
A spectral library will be generated
Deep learning will be used to generate a new in silico spectral library from peptides provided
Cysteine carbamidomethylation enabled as a fixed modification
Modification UniMod:35 with mass delta 15.9949 at M will be considered as variable
Maximum number of variable modifications set to 1
A spectral library will be generated
DIA-NN will carry out FASTA digest for in silico lib generation
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
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
WARNING: peptidoform scoring enabled because variable modifications have been declared; to disable, use --no-peptidoforms
The following variable modifications will be scored: UniMod:35 

6 files will be processed
[0:00] Loading FASTA /public/local/ProteoBench/fastas/ProteoBenchFASTA_MixedSpecies_HYE.fasta
[0:10] Processing FASTA
[0:19] Assembling elution groups
[0:30] 5055014 precursors generated
[0:30] Protein names missing for some isoforms
[0:30] Gene names missing for some isoforms
[0:30] Library contains 31685 proteins, and 0 genes
[0:40] [1:00] [29:18] [31:18] [31:25] [31:29] Saving the library to /home/robbe/PB_output/results/Astral_DIANN_version_comparison/HYE_Astral/diann_v1.9.2/report-lib.predicted.speclib
[31:38] Initialising library
[31:51] Loading spectral library /home/robbe/PB_output/results/Astral_DIANN_version_comparison/HYE_Astral/diann_v1.9.2/report-lib.predicted.speclib
[31:55] Library annotated with sequence database(s): /public/local/ProteoBench/fastas/ProteoBenchFASTA_MixedSpecies_HYE.fasta
[31:56] Spectral library loaded: 31837 protein isoforms, 51483 protein groups and 5055014 precursors in 2602499 elution groups.
[31:56] Loading protein annotations from FASTA /public/local/ProteoBench/fastas/ProteoBenchFASTA_MixedSpecies_HYE.fasta
[31:57] Annotating library proteins with information from the FASTA database
[31:57] Protein names missing for some isoforms
[31:57] Gene names missing for some isoforms
[31:57] Library contains 31685 proteins, and 0 genes
[32:01] Initialising library

First pass: generating a spectral library from DIA data

[32:15] File #1/6
[32:15] Loading run /public/local/ProteoBench/HYE_Astral_mzml/LFQ_Astral_DIA_15min_50ng_Condition_A_REP1.mzML
[33:37] 5050223 library precursors are potentially detectable
[33:38] Calibrating with mass accuracies 30 (MS1), 20 (MS2)
[34:18] RT window set to 0.860652
[34:18] Peak width: 2.844
[34:18] Scan window radius set to 6
[34:18] Recommended MS1 mass accuracy setting: 2.31864 ppm
[35:21] Optimised mass accuracy: 8.29899 ppm
[37:34] Removing low confidence identifications
[38:33] Precursors at 1% peptidoform FDR: 61474
[38:35] Removing interfering precursors
[38:46] Training neural networks on 329521 PSMs
[38:59] Number of IDs at 0.01 FDR: 98525
[39:04] Precursors at 1% peptidoform FDR: 81964
[39:05] Calculating protein q-values
[39:06] Number of genes identified at 1% FDR: 0 (precursor-level), 0 (protein-level) (inference performed using proteotypic peptides only)
[39:06] Quantification
[39:07] Precursors with monitored PTMs at 1% FDR: 145 out of 19957 considered
[39:07] Unmodified precursors with monitored PTM sites at 1% FDR: 15821
[39:07] Precursors with PTMs localised (when required) with > 90% confidence: 86 out of 145
[39:08] Quantification information saved to /home/robbe/PB_output/results/Astral_DIANN_version_comparison/HYE_Astral/diann_v1.9.2/_public_local_ProteoBench_HYE_Astral_mzml_LFQ_Astral_DIA_15min_50ng_Condition_A_REP1_mzML.quant

[39:08] File #2/6
[39:08] Loading run /public/local/ProteoBench/HYE_Astral_mzml/LFQ_Astral_DIA_15min_50ng_Condition_A_REP2.mzML
[40:28] 5050223 library precursors are potentially detectable
[40:29] Calibrating with mass accuracies 30 (MS1), 18.8902 (MS2)
[41:12] RT window set to 0.844324
[41:12] Recommended MS1 mass accuracy setting: 2.59555 ppm
[42:40] Removing low confidence identifications
[43:37] Precursors at 1% peptidoform FDR: 62024
[43:38] Removing interfering precursors
[43:50] Training neural networks on 328005 PSMs
[44:03] Number of IDs at 0.01 FDR: 98581
[44:09] Precursors at 1% peptidoform FDR: 85042
[44:10] Calculating protein q-values
[44:11] Number of genes identified at 1% FDR: 0 (precursor-level), 0 (protein-level) (inference performed using proteotypic peptides only)
[44:11] Quantification
[44:12] Precursors with monitored PTMs at 1% FDR: 304 out of 20095 considered
[44:12] Unmodified precursors with monitored PTM sites at 1% FDR: 16115
[44:12] Precursors with PTMs localised (when required) with > 90% confidence: 243 out of 304
[44:13] Quantification information saved to /home/robbe/PB_output/results/Astral_DIANN_version_comparison/HYE_Astral/diann_v1.9.2/_public_local_ProteoBench_HYE_Astral_mzml_LFQ_Astral_DIA_15min_50ng_Condition_A_REP2_mzML.quant

[44:13] File #3/6
[44:13] Loading run /public/local/ProteoBench/HYE_Astral_mzml/LFQ_Astral_DIA_15min_50ng_Condition_A_REP3.mzML
[45:33] 5050223 library precursors are potentially detectable
[45:34] Calibrating with mass accuracies 30 (MS1), 18.2515 (MS2)
[46:25] RT window set to 0.833746
[46:25] Recommended MS1 mass accuracy setting: 2.34887 ppm
[48:35] Removing low confidence identifications
[49:46] Precursors at 1% peptidoform FDR: 61646
[49:47] Removing interfering precursors
[49:58] Training neural networks on 326315 PSMs
[50:13] Number of IDs at 0.01 FDR: 99270
[50:19] Precursors at 1% peptidoform FDR: 83616
[50:20] Calculating protein q-values
[50:20] Number of genes identified at 1% FDR: 0 (precursor-level), 0 (protein-level) (inference performed using proteotypic peptides only)
[50:20] Quantification
[50:21] Precursors with monitored PTMs at 1% FDR: 605 out of 20468 considered
[50:21] Unmodified precursors with monitored PTM sites at 1% FDR: 15805
[50:21] Precursors with PTMs localised (when required) with > 90% confidence: 537 out of 605
[50:22] Quantification information saved to /home/robbe/PB_output/results/Astral_DIANN_version_comparison/HYE_Astral/diann_v1.9.2/_public_local_ProteoBench_HYE_Astral_mzml_LFQ_Astral_DIA_15min_50ng_Condition_A_REP3_mzML.quant

[50:22] File #4/6
[50:22] Loading run /public/local/ProteoBench/HYE_Astral_mzml/LFQ_Astral_DIA_15min_50ng_Condition_B_REP1.mzML
[51:58] 5050223 library precursors are potentially detectable
[51:59] Calibrating with mass accuracies 30 (MS1), 18.8784 (MS2)
[52:45] RT window set to 0.861309
[52:45] Recommended MS1 mass accuracy setting: 2.27945 ppm
[55:06] Removing low confidence identifications
[56:25] Precursors at 1% peptidoform FDR: 63398
[56:28] Removing interfering precursors
[56:40] Training neural networks on 335719 PSMs
[56:53] Number of IDs at 0.01 FDR: 99437
[56:59] Precursors at 1% peptidoform FDR: 83548
[57:00] Calculating protein q-values
[57:01] Number of genes identified at 1% FDR: 0 (precursor-level), 0 (protein-level) (inference performed using proteotypic peptides only)
[57:01] Quantification
[57:02] Precursors with monitored PTMs at 1% FDR: 521 out of 21080 considered
[57:02] Unmodified precursors with monitored PTM sites at 1% FDR: 16245
[57:02] Precursors with PTMs localised (when required) with > 90% confidence: 434 out of 521
[57:03] Quantification information saved to /home/robbe/PB_output/results/Astral_DIANN_version_comparison/HYE_Astral/diann_v1.9.2/_public_local_ProteoBench_HYE_Astral_mzml_LFQ_Astral_DIA_15min_50ng_Condition_B_REP1_mzML.quant

[57:03] File #5/6
[57:03] Loading run /public/local/ProteoBench/HYE_Astral_mzml/LFQ_Astral_DIA_15min_50ng_Condition_B_REP2.mzML
[58:33] 5050223 library precursors are potentially detectable
[58:34] Calibrating with mass accuracies 30 (MS1), 19.0577 (MS2)
[59:11] RT window set to 0.823291
[59:11] Recommended MS1 mass accuracy setting: 2.2459 ppm
[61:05] Removing low confidence identifications
[62:08] Precursors at 1% peptidoform FDR: 63628
[62:10] Removing interfering precursors
[62:22] Training neural networks on 335326 PSMs
[62:34] Number of IDs at 0.01 FDR: 98979
[62:38] Precursors at 1% peptidoform FDR: 84298
[62:40] Calculating protein q-values
[62:40] Number of genes identified at 1% FDR: 0 (precursor-level), 0 (protein-level) (inference performed using proteotypic peptides only)
[62:40] Quantification
[62:41] Precursors with monitored PTMs at 1% FDR: 672 out of 20821 considered
[62:41] Unmodified precursors with monitored PTM sites at 1% FDR: 16324
[62:41] Precursors with PTMs localised (when required) with > 90% confidence: 598 out of 672
[62:43] Quantification information saved to /home/robbe/PB_output/results/Astral_DIANN_version_comparison/HYE_Astral/diann_v1.9.2/_public_local_ProteoBench_HYE_Astral_mzml_LFQ_Astral_DIA_15min_50ng_Condition_B_REP2_mzML.quant

[62:43] File #6/6
[62:43] Loading run /public/local/ProteoBench/HYE_Astral_mzml/LFQ_Astral_DIA_15min_50ng_Condition_B_REP3.mzML
[64:16] 5050223 library precursors are potentially detectable
[64:17] Calibrating with mass accuracies 30 (MS1), 19.033 (MS2)
[64:54] RT window set to 0.835511
[64:54] Recommended MS1 mass accuracy setting: 2.3065 ppm
[67:04] Removing low confidence identifications
[68:28] Precursors at 1% peptidoform FDR: 63564
[68:30] Removing interfering precursors
[68:41] Training neural networks on 339806 PSMs
[68:57] Number of IDs at 0.01 FDR: 100365
[69:02] Precursors at 1% peptidoform FDR: 84376
[69:04] Calculating protein q-values
[69:05] Number of genes identified at 1% FDR: 0 (precursor-level), 0 (protein-level) (inference performed using proteotypic peptides only)
[69:05] Quantification
[69:06] Precursors with monitored PTMs at 1% FDR: 237 out of 21279 considered
[69:06] Unmodified precursors with monitored PTM sites at 1% FDR: 16397
[69:06] Precursors with PTMs localised (when required) with > 90% confidence: 154 out of 237
[69:07] Quantification information saved to /home/robbe/PB_output/results/Astral_DIANN_version_comparison/HYE_Astral/diann_v1.9.2/_public_local_ProteoBench_HYE_Astral_mzml_LFQ_Astral_DIA_15min_50ng_Condition_B_REP3_mzML.quant

[69:07] Cross-run analysis
[69:07] Reading quantification information: 6 files
[69:14] Quantifying peptides
[69:57] Assembling protein groups
[70:01] Quantifying proteins
[70:02] Calculating q-values for protein and gene groups
[70:03] Calculating global q-values for protein and gene groups
[70:03] Protein groups with global q-value <= 0.01: 13170
[70:07] Compressed report saved to /home/robbe/PB_output/results/Astral_DIANN_version_comparison/HYE_Astral/diann_v1.9.2/report-first-pass.parquet. Use R 'arrow' or Python 'PyArrow' package to process
[70:07] Writing report
[70:21] Report saved to /home/robbe/PB_output/results/Astral_DIANN_version_comparison/HYE_Astral/diann_v1.9.2/report-first-pass.tsv.
[70:21] Stats report saved to /home/robbe/PB_output/results/Astral_DIANN_version_comparison/HYE_Astral/diann_v1.9.2/report-first-pass.stats.tsv
[70:21] Generating spectral library:
[70:23] 124357 target and 1175 decoy precursors saved
WARNING: 2298 precursors without any fragments annotated were skipped
[70:23] Spectral library saved to /home/robbe/PB_output/results/Astral_DIANN_version_comparison/HYE_Astral/diann_v1.9.2/report-lib.parquet

[70:24] Loading spectral library /home/robbe/PB_output/results/Astral_DIANN_version_comparison/HYE_Astral/diann_v1.9.2/report-lib.parquet
[70:25] Spectral library loaded: 13816 protein isoforms, 14793 protein groups and 125532 precursors in 117042 elution groups.
[70:25] Loading protein annotations from FASTA /public/local/ProteoBench/fastas/ProteoBenchFASTA_MixedSpecies_HYE.fasta
[70:26] Annotating library proteins with information from the FASTA database
[70:26] Protein names missing for some isoforms
[70:26] Gene names missing for some isoforms
[70:26] Library contains 13794 proteins, and 0 genes
[70:26] Initialising library
[70:26] Saving the library to /home/robbe/PB_output/results/Astral_DIANN_version_comparison/HYE_Astral/diann_v1.9.2/report-lib.parquet.skyline.speclib


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

[70:27] File #1/6
[70:27] Loading run /public/local/ProteoBench/HYE_Astral_mzml/LFQ_Astral_DIA_15min_50ng_Condition_A_REP1.mzML
[72:08] 124357 library precursors are potentially detectable
[72:08] Calibrating with mass accuracies 30 (MS1), 18.0599 (MS2)
[72:09] RT window set to 0.426291
[72:09] Recommended MS1 mass accuracy setting: 2.80469 ppm
[72:13] Removing low confidence identifications
[72:19] Precursors at 1% peptidoform FDR: 74787
[72:20] Removing interfering precursors
[72:21] Training neural networks on 165085 PSMs
[72:28] Number of IDs at 0.01 FDR: 107017
[72:34] Precursors at 1% peptidoform FDR: 91580
[72:34] Calculating protein q-values
[72:34] Number of genes identified at 1% FDR: 0 (precursor-level), 0 (protein-level) (inference performed using proteotypic peptides only)
[72:34] Quantification
[72:34] Precursors with monitored PTMs at 1% FDR: 364 out of 20363 considered
[72:34] Unmodified precursors with monitored PTM sites at 1% FDR: 17467
[72:34] Precursors with PTMs localised (when required) with > 90% confidence: 299 out of 364

[72:35] File #2/6
[72:35] Loading run /public/local/ProteoBench/HYE_Astral_mzml/LFQ_Astral_DIA_15min_50ng_Condition_A_REP2.mzML
[73:56] 124357 library precursors are potentially detectable
[73:56] Calibrating with mass accuracies 30 (MS1), 18.3888 (MS2)
[73:57] RT window set to 0.446212
[73:57] Recommended MS1 mass accuracy setting: 2.74648 ppm
[74:01] Removing low confidence identifications
[74:06] Precursors at 1% peptidoform FDR: 76677
[74:06] Removing interfering precursors
[74:08] Training neural networks on 165288 PSMs
[74:14] Number of IDs at 0.01 FDR: 107417
[74:19] Precursors at 1% peptidoform FDR: 92517
[74:19] Calculating protein q-values
[74:19] Number of genes identified at 1% FDR: 0 (precursor-level), 0 (protein-level) (inference performed using proteotypic peptides only)
[74:19] Quantification
[74:19] Precursors with monitored PTMs at 1% FDR: 362 out of 20460 considered
[74:19] Unmodified precursors with monitored PTM sites at 1% FDR: 17650
[74:19] Precursors with PTMs localised (when required) with > 90% confidence: 304 out of 362

[74:20] File #3/6
[74:20] Loading run /public/local/ProteoBench/HYE_Astral_mzml/LFQ_Astral_DIA_15min_50ng_Condition_A_REP3.mzML
[75:57] 124357 library precursors are potentially detectable
[75:57] Calibrating with mass accuracies 30 (MS1), 17.7637 (MS2)
[75:59] RT window set to 0.42878
[75:59] Recommended MS1 mass accuracy setting: 2.76034 ppm
[76:03] Removing low confidence identifications
[76:08] Precursors at 1% peptidoform FDR: 75512
[76:09] Removing interfering precursors
[76:10] Training neural networks on 165078 PSMs
[76:17] Number of IDs at 0.01 FDR: 106918
[76:21] Precursors at 1% peptidoform FDR: 91440
[76:22] Calculating protein q-values
[76:22] Number of genes identified at 1% FDR: 0 (precursor-level), 0 (protein-level) (inference performed using proteotypic peptides only)
[76:22] Quantification
[76:22] Precursors with monitored PTMs at 1% FDR: 371 out of 20131 considered
[76:22] Unmodified precursors with monitored PTM sites at 1% FDR: 17332
[76:22] Precursors with PTMs localised (when required) with > 90% confidence: 309 out of 371

[76:23] File #4/6
[76:23] Loading run /public/local/ProteoBench/HYE_Astral_mzml/LFQ_Astral_DIA_15min_50ng_Condition_B_REP1.mzML
[77:48] 124357 library precursors are potentially detectable
[77:48] Calibrating with mass accuracies 30 (MS1), 18.4238 (MS2)
[77:49] RT window set to 0.411336
[77:49] Recommended MS1 mass accuracy setting: 2.85695 ppm
[77:53] Removing low confidence identifications
[77:58] Precursors at 1% peptidoform FDR: 77415
[77:59] Removing interfering precursors
[78:00] Training neural networks on 166102 PSMs
[78:06] Number of IDs at 0.01 FDR: 108175
[78:10] Precursors at 1% peptidoform FDR: 92376
[78:10] Calculating protein q-values
[78:10] Number of genes identified at 1% FDR: 0 (precursor-level), 0 (protein-level) (inference performed using proteotypic peptides only)
[78:10] Quantification
[78:11] Precursors with monitored PTMs at 1% FDR: 389 out of 20641 considered
[78:11] Unmodified precursors with monitored PTM sites at 1% FDR: 17728
[78:11] Precursors with PTMs localised (when required) with > 90% confidence: 331 out of 389

[78:11] File #5/6
[78:11] Loading run /public/local/ProteoBench/HYE_Astral_mzml/LFQ_Astral_DIA_15min_50ng_Condition_B_REP2.mzML
[79:33] 124357 library precursors are potentially detectable
[79:33] Calibrating with mass accuracies 30 (MS1), 18.551 (MS2)
[79:34] RT window set to 0.457284
[79:34] Recommended MS1 mass accuracy setting: 2.74612 ppm
[79:37] Removing low confidence identifications
[79:42] Precursors at 1% peptidoform FDR: 77010
[79:42] Removing interfering precursors
[79:44] Training neural networks on 166153 PSMs
[79:49] Number of IDs at 0.01 FDR: 108844
[79:54] Precursors at 1% peptidoform FDR: 93434
[79:54] Calculating protein q-values
[79:54] Number of genes identified at 1% FDR: 0 (precursor-level), 0 (protein-level) (inference performed using proteotypic peptides only)
[79:54] Quantification
[79:54] Precursors with monitored PTMs at 1% FDR: 394 out of 20848 considered
[79:54] Unmodified precursors with monitored PTM sites at 1% FDR: 17926
[79:54] Precursors with PTMs localised (when required) with > 90% confidence: 338 out of 394

[79:55] File #6/6
[79:55] Loading run /public/local/ProteoBench/HYE_Astral_mzml/LFQ_Astral_DIA_15min_50ng_Condition_B_REP3.mzML
[81:26] 124357 library precursors are potentially detectable
[81:26] Calibrating with mass accuracies 30 (MS1), 18.6883 (MS2)
[81:28] RT window set to 0.43254
[81:28] Recommended MS1 mass accuracy setting: 2.69852 ppm
[81:31] Removing low confidence identifications
[81:36] Precursors at 1% peptidoform FDR: 76856
[81:36] Removing interfering precursors
[81:38] Training neural networks on 165901 PSMs
[81:44] Number of IDs at 0.01 FDR: 107719
[81:49] Precursors at 1% peptidoform FDR: 92059
[81:49] Calculating protein q-values
[81:49] Number of genes identified at 1% FDR: 0 (precursor-level), 0 (protein-level) (inference performed using proteotypic peptides only)
[81:49] Quantification
[81:50] Precursors with monitored PTMs at 1% FDR: 236 out of 20464 considered
[81:50] Unmodified precursors with monitored PTM sites at 1% FDR: 17728
[81:50] Precursors with PTMs localised (when required) with > 90% confidence: 173 out of 236

[81:50] Cross-run analysis
[81:50] Reading quantification information: 6 files
[81:53] Quantifying peptides
[83:38] Quantification parameters: 0.309196, 0.00148314, 0.00133304, 0.0121494, 0.0115088, 0.0121043, 0.165398, 0.212993, 0.163777, 0.0134753, 0.0374074, 0.0152024, 0.346035, 0.0524923, 0.0741123, 0.0116388
[83:59] Quantifying proteins
[83:59] Calculating q-values for protein and gene groups
[83:59] Calculating global q-values for protein and gene groups
[83:59] Protein groups with global q-value <= 0.01: 13303
[84:04] Compressed report saved to /home/robbe/PB_output/results/Astral_DIANN_version_comparison/HYE_Astral/diann_v1.9.2/report.parquet. Use R 'arrow' or Python 'PyArrow' package to process
[84:04] Writing report
[84:16] Report saved to /home/robbe/PB_output/results/Astral_DIANN_version_comparison/HYE_Astral/diann_v1.9.2/report.tsv.
[84:16] Stats report saved to /home/robbe/PB_output/results/Astral_DIANN_version_comparison/HYE_Astral/diann_v1.9.2/report.stats.tsv

