DIA-NN 1.9.1 (Data-Independent Acquisition by Neural Networks)
Compiled on Jul 15 2024 09:42:01
Current date and time: Wed Apr 16 14:40:55 2025
Logical CPU cores: 128
diann-1.9.1/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_1.9.1_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
[0:07] Processing FASTA
[0:15] Assembling elution groups
[0:26] 5116692 precursors generated
[0:26] Protein names missing for some isoforms
[0:26] Gene names missing for some isoforms
[0:26] Library contains 31685 proteins, and 0 genes
[0:36] [0:58] [4:25] [4:53] [5:29] [5:35] Saving the library to report-lib.predicted.speclib
[6:00] Initialising library
[6:15] Loading spectral library report-lib.predicted.speclib
[6:23] Library annotated with sequence database(s): ProteoBenchFASTA_DDAQuantification.fasta
[6:25] Spectral library loaded: 31837 protein isoforms, 51765 protein groups and 5116692 precursors in 2716663 elution groups.
[6:25] Loading protein annotations from FASTA ProteoBenchFASTA_DDAQuantification.fasta
[6:25] Annotating library proteins with information from the FASTA database
[6:25] Protein names missing for some isoforms
[6:25] Gene names missing for some isoforms
[6:25] Library contains 31685 proteins, and 0 genes
[6:37] [6:51] [10:28] [11:05] [11:12] [11:18] Saving the library to report-lib.predicted.speclib
[11:43] Initialising library

First pass: generating a spectral library from DIA data

[11:56] File #1/6
[11:56] Loading run Raw_Astral/mzmls/LFQ_Astral_DIA_15min_50ng_Condition_A_REP1.mzML
[13:09] 5020855 library precursors are potentially detectable
[13:10] Processing...
[13:23] RT window set to 0.85924
[13:23] Peak width: 2.716
[13:23] Scan window radius set to 5
[13:23] Recommended MS1 mass accuracy setting: 2.35167 ppm
[13:34] Optimised mass accuracy: 9.60848 ppm
[14:05] Removing low confidence identifications
[14:19] Precursors at 1% peptidoform FDR: 61176
[14:20] Removing interfering precursors
[14:26] Training neural networks: 197674 targets, 118384 decoys
[14:36] Number of IDs at 0.01 FDR: 118744
[14:49] Precursors at 1% peptidoform FDR: 83928
[14:50] Calculating protein q-values
[14:51] Number of genes identified at 1% FDR: 0 (precursor-level), 0 (protein-level) (inference performed using proteotypic peptides only)
[14:51] Quantification
[14:52] Precursors with monitored PTMs at 1% FDR: 2330 out of 44712 considered
[14:52] Unmodified precursors with monitored PTM sites at 1% FDR: 15756
[14:52] Precursors with PTMs localised (when required) with > 90% confidence: 2288 out of 2330
[14:54] Quantification information saved to Raw_Astral/mzmls/LFQ_Astral_DIA_15min_50ng_Condition_A_REP1.mzML.quant

[14:54] File #2/6
[14:54] Loading run Raw_Astral/mzmls/LFQ_Astral_DIA_15min_50ng_Condition_A_REP2.mzML
[15:41] 5020855 library precursors are potentially detectable
[15:42] Processing...
[15:57] RT window set to 0.865047
[15:57] Recommended MS1 mass accuracy setting: 2.24818 ppm
[16:31] Removing low confidence identifications
[16:44] Precursors at 1% peptidoform FDR: 61808
[16:45] Removing interfering precursors
[16:51] Training neural networks: 201113 targets, 120762 decoys
[17:02] Number of IDs at 0.01 FDR: 120937
[17:15] Precursors at 1% peptidoform FDR: 83750
[17:16] Calculating protein q-values
[17:17] Number of genes identified at 1% FDR: 0 (precursor-level), 0 (protein-level) (inference performed using proteotypic peptides only)
[17:17] Quantification
[17:17] Precursors with monitored PTMs at 1% FDR: 2263 out of 45544 considered
[17:17] Unmodified precursors with monitored PTM sites at 1% FDR: 15727
[17:17] Precursors with PTMs localised (when required) with > 90% confidence: 2220 out of 2263
[17:19] Quantification information saved to Raw_Astral/mzmls/LFQ_Astral_DIA_15min_50ng_Condition_A_REP2.mzML.quant

[17:19] File #3/6
[17:19] Loading run Raw_Astral/mzmls/LFQ_Astral_DIA_15min_50ng_Condition_A_REP3.mzML
[18:11] 5020855 library precursors are potentially detectable
[18:12] Processing...
[18:24] RT window set to 0.811078
[18:24] Recommended MS1 mass accuracy setting: 2.34192 ppm
[19:01] Removing low confidence identifications
[19:12] Precursors at 1% peptidoform FDR: 61750
[19:13] Removing interfering precursors
[19:19] Training neural networks: 198828 targets, 120011 decoys
[19:29] Number of IDs at 0.01 FDR: 119975
[19:43] Precursors at 1% peptidoform FDR: 83587
[19:44] Calculating protein q-values
[19:44] Number of genes identified at 1% FDR: 0 (precursor-level), 0 (protein-level) (inference performed using proteotypic peptides only)
[19:44] Quantification
[19:45] Precursors with monitored PTMs at 1% FDR: 2175 out of 45126 considered
[19:45] Unmodified precursors with monitored PTM sites at 1% FDR: 15411
[19:45] Precursors with PTMs localised (when required) with > 90% confidence: 2136 out of 2175
[19:46] Quantification information saved to Raw_Astral/mzmls/LFQ_Astral_DIA_15min_50ng_Condition_A_REP3.mzML.quant

[19:46] File #4/6
[19:46] Loading run /home/robbe/ProteoBench_diaPASEF/Raw_Astral/mzmls/LFQ_Astral_DIA_15min_50ng_Condition_B_REP1.mzML
[20:41] 5020855 library precursors are potentially detectable
[20:42] Processing...
[20:54] RT window set to 0.796351
[20:54] Recommended MS1 mass accuracy setting: 2.1449 ppm
[21:29] Removing low confidence identifications
[21:44] Precursors at 1% peptidoform FDR: 63530
[21:45] Removing interfering precursors
[21:51] Training neural networks: 203176 targets, 122838 decoys
[22:01] Number of IDs at 0.01 FDR: 123466
[22:14] Precursors at 1% peptidoform FDR: 83135
[22:15] Calculating protein q-values
[22:16] Number of genes identified at 1% FDR: 0 (precursor-level), 0 (protein-level) (inference performed using proteotypic peptides only)
[22:16] Quantification
[22:16] Precursors with monitored PTMs at 1% FDR: 2655 out of 48212 considered
[22:16] Unmodified precursors with monitored PTM sites at 1% FDR: 15611
[22:16] Precursors with PTMs localised (when required) with > 90% confidence: 2611 out of 2655
[22:18] Quantification information saved to /home/robbe/ProteoBench_diaPASEF/Raw_Astral/mzmls/LFQ_Astral_DIA_15min_50ng_Condition_B_REP1.mzML.quant

[22:18] File #5/6
[22:18] Loading run /home/robbe/ProteoBench_diaPASEF/Raw_Astral/mzmls/LFQ_Astral_DIA_15min_50ng_Condition_B_REP2.mzML
[23:19] 5020855 library precursors are potentially detectable
[23:20] Processing...
[23:35] RT window set to 0.903348
[23:35] Recommended MS1 mass accuracy setting: 2.21391 ppm
[24:15] Removing low confidence identifications
[24:27] Precursors at 1% peptidoform FDR: 64031
[24:28] Removing interfering precursors
[24:35] Training neural networks: 204144 targets, 122930 decoys
[24:45] Number of IDs at 0.01 FDR: 123831
[24:59] Precursors at 1% peptidoform FDR: 85438
[25:00] Calculating protein q-values
[25:01] Number of genes identified at 1% FDR: 0 (precursor-level), 0 (protein-level) (inference performed using proteotypic peptides only)
[25:01] Quantification
[25:01] Precursors with monitored PTMs at 1% FDR: 2866 out of 47917 considered
[25:01] Unmodified precursors with monitored PTM sites at 1% FDR: 15954
[25:02] Precursors with PTMs localised (when required) with > 90% confidence: 2817 out of 2866
[25:03] Quantification information saved to /home/robbe/ProteoBench_diaPASEF/Raw_Astral/mzmls/LFQ_Astral_DIA_15min_50ng_Condition_B_REP2.mzML.quant

[25:03] File #6/6
[25:03] Loading run /home/robbe/ProteoBench_diaPASEF/Raw_Astral/mzmls/LFQ_Astral_DIA_15min_50ng_Condition_B_REP3.mzML
[26:01] 5020855 library precursors are potentially detectable
[26:03] Processing...
[26:15] RT window set to 0.823049
[26:16] Recommended MS1 mass accuracy setting: 2.38684 ppm
[26:49] Removing low confidence identifications
[27:05] Precursors at 1% peptidoform FDR: 63073
[27:06] Removing interfering precursors
[27:13] Training neural networks: 201640 targets, 121626 decoys
[27:23] Number of IDs at 0.01 FDR: 122647
[27:37] Precursors at 1% peptidoform FDR: 84179
[27:38] Calculating protein q-values
[27:38] Number of genes identified at 1% FDR: 0 (precursor-level), 0 (protein-level) (inference performed using proteotypic peptides only)
[27:38] Quantification
[27:39] Precursors with monitored PTMs at 1% FDR: 2777 out of 47547 considered
[27:39] Unmodified precursors with monitored PTM sites at 1% FDR: 15878
[27:39] Precursors with PTMs localised (when required) with > 90% confidence: 2722 out of 2777
[27:41] Quantification information saved to /home/robbe/ProteoBench_diaPASEF/Raw_Astral/mzmls/LFQ_Astral_DIA_15min_50ng_Condition_B_REP3.mzML.quant

[27:41] Cross-run analysis
[27:41] Reading quantification information: 6 files
[27:50] Quantifying peptides
[28:44] Assembling protein groups
[28:47] Quantifying proteins
[28:48] Calculating q-values for protein and gene groups
[28:50] Calculating global q-values for protein and gene groups
[28:50] Protein groups with global q-value <= 0.01: 25834
[28:55] Compressed report saved to run_output_Astral/diann_1.9.1_default/report-first-pass.parquet. Use R 'arrow' or Python 'PyArrow' package to process
[28:56] Writing report
[29:18] Report saved to run_output_Astral/diann_1.9.1_default/report-first-pass.tsv.
[29:18] Stats report saved to run_output_Astral/diann_1.9.1_default/report-first-pass.stats.tsv
[29:18] Generating spectral library:
[29:22] 208473 target and 2313 decoy precursors saved
[29:22] Spectral library saved to report-lib.parquet

[29:25] Loading spectral library report-lib.parquet
[29:27] Spectral library loaded: 26516 protein isoforms, 26153 protein groups and 210786 precursors in 202684 elution groups.
[29:28] Loading protein annotations from FASTA ProteoBenchFASTA_DDAQuantification.fasta
[29:28] Annotating library proteins with information from the FASTA database
[29:28] Protein names missing for some isoforms
[29:28] Gene names missing for some isoforms
[29:28] Library contains 26447 proteins, and 0 genes
[29:28] Initialising library
[29:29] Saving the library to report-lib.parquet.skyline.speclib


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

[29:29] File #1/6
[29:29] Loading run Raw_Astral/mzmls/LFQ_Astral_DIA_15min_50ng_Condition_A_REP1.mzML
[30:33] 208473 library precursors are potentially detectable
[30:33] Processing...
[30:34] RT window set to 0.331602
[30:34] Recommended MS1 mass accuracy setting: 2.48118 ppm
[30:36] Removing low confidence identifications
[30:37] Precursors at 1% peptidoform FDR: 64734
[30:38] Removing interfering precursors
[30:39] Training neural networks: 154823 targets, 82924 decoys
[30:45] Number of IDs at 0.01 FDR: 108514
[30:53] Precursors at 1% peptidoform FDR: 87950
[30:53] Calculating protein q-values
[30:53] Number of genes identified at 1% FDR: 0 (precursor-level), 0 (protein-level) (inference performed using proteotypic peptides only)
[30:53] Quantification
[30:53] Precursors with monitored PTMs at 1% FDR: 3008 out of 31940 considered
[30:53] Unmodified precursors with monitored PTM sites at 1% FDR: 16667
[30:53] Precursors with PTMs localised (when required) with > 90% confidence: 2951 out of 3008

[30:54] File #2/6
[30:54] Loading run Raw_Astral/mzmls/LFQ_Astral_DIA_15min_50ng_Condition_A_REP2.mzML
[31:45] 208473 library precursors are potentially detectable
[31:45] Processing...
[31:46] RT window set to 0.298953
[31:46] Recommended MS1 mass accuracy setting: 2.22915 ppm
[31:49] Removing low confidence identifications
[31:50] Precursors at 1% peptidoform FDR: 63940
[31:50] Removing interfering precursors
[31:52] Training neural networks: 156072 targets, 83646 decoys
[31:59] Number of IDs at 0.01 FDR: 108730
[32:06] Precursors at 1% peptidoform FDR: 88071
[32:06] Calculating protein q-values
[32:06] Number of genes identified at 1% FDR: 0 (precursor-level), 0 (protein-level) (inference performed using proteotypic peptides only)
[32:06] Quantification
[32:07] Precursors with monitored PTMs at 1% FDR: 3004 out of 31776 considered
[32:07] Unmodified precursors with monitored PTM sites at 1% FDR: 16680
[32:07] Precursors with PTMs localised (when required) with > 90% confidence: 2948 out of 3004

[32:07] File #3/6
[32:07] Loading run Raw_Astral/mzmls/LFQ_Astral_DIA_15min_50ng_Condition_A_REP3.mzML
[33:22] 208473 library precursors are potentially detectable
[33:22] Processing...
[33:23] RT window set to 0.304728
[33:23] Recommended MS1 mass accuracy setting: 2.56122 ppm
[33:25] Removing low confidence identifications
[33:25] Precursors at 1% peptidoform FDR: 64653
[33:26] Removing interfering precursors
[33:27] Training neural networks: 155229 targets, 83134 decoys
[33:32] Number of IDs at 0.01 FDR: 107452
[33:37] Precursors at 1% peptidoform FDR: 88002
[33:37] Calculating protein q-values
[33:37] Number of genes identified at 1% FDR: 0 (precursor-level), 0 (protein-level) (inference performed using proteotypic peptides only)
[33:37] Quantification
[33:37] Precursors with monitored PTMs at 1% FDR: 3024 out of 30526 considered
[33:37] Unmodified precursors with monitored PTM sites at 1% FDR: 16559
[33:37] Precursors with PTMs localised (when required) with > 90% confidence: 2974 out of 3024

[33:37] File #4/6
[33:37] Loading run /home/robbe/ProteoBench_diaPASEF/Raw_Astral/mzmls/LFQ_Astral_DIA_15min_50ng_Condition_B_REP1.mzML
[34:19] 208473 library precursors are potentially detectable
[34:19] Processing...
[34:19] RT window set to 0.319099
[34:19] Recommended MS1 mass accuracy setting: 2.27194 ppm
[34:21] Removing low confidence identifications
[34:21] Precursors at 1% peptidoform FDR: 65354
[34:22] Removing interfering precursors
[34:23] Training neural networks: 157521 targets, 84403 decoys
[34:26] Number of IDs at 0.01 FDR: 110604
[34:31] Precursors at 1% peptidoform FDR: 88625
[34:31] Calculating protein q-values
[34:31] Number of genes identified at 1% FDR: 0 (precursor-level), 0 (protein-level) (inference performed using proteotypic peptides only)
[34:31] Quantification
[34:31] Precursors with monitored PTMs at 1% FDR: 3206 out of 33302 considered
[34:31] Unmodified precursors with monitored PTM sites at 1% FDR: 16824
[34:31] Precursors with PTMs localised (when required) with > 90% confidence: 3166 out of 3206

[34:32] File #5/6
[34:32] Loading run /home/robbe/ProteoBench_diaPASEF/Raw_Astral/mzmls/LFQ_Astral_DIA_15min_50ng_Condition_B_REP2.mzML
[35:05] 208473 library precursors are potentially detectable
[35:05] Processing...
[35:05] RT window set to 0.31034
[35:05] Recommended MS1 mass accuracy setting: 2.27337 ppm
[35:07] Removing low confidence identifications
[35:07] Precursors at 1% peptidoform FDR: 65008
[35:07] Removing interfering precursors
[35:08] Training neural networks: 156637 targets, 83913 decoys
[35:11] Number of IDs at 0.01 FDR: 110321
[35:16] Precursors at 1% peptidoform FDR: 88702
[35:16] Calculating protein q-values
[35:16] Number of genes identified at 1% FDR: 0 (precursor-level), 0 (protein-level) (inference performed using proteotypic peptides only)
[35:16] Quantification
[35:16] Precursors with monitored PTMs at 1% FDR: 3201 out of 32766 considered
[35:16] Unmodified precursors with monitored PTM sites at 1% FDR: 16697
[35:16] Precursors with PTMs localised (when required) with > 90% confidence: 3146 out of 3201

[35:17] File #6/6
[35:17] Loading run /home/robbe/ProteoBench_diaPASEF/Raw_Astral/mzmls/LFQ_Astral_DIA_15min_50ng_Condition_B_REP3.mzML
[36:09] 208473 library precursors are potentially detectable
[36:09] Processing...
[36:10] RT window set to 0.313573
[36:10] Recommended MS1 mass accuracy setting: 2.48322 ppm
[36:13] Removing low confidence identifications
[36:13] Precursors at 1% peptidoform FDR: 65507
[36:14] Removing interfering precursors
[36:16] Training neural networks: 156522 targets, 83918 decoys
[36:22] Number of IDs at 0.01 FDR: 111011
[36:30] Precursors at 1% peptidoform FDR: 88879
[36:30] Calculating protein q-values
[36:30] Number of genes identified at 1% FDR: 0 (precursor-level), 0 (protein-level) (inference performed using proteotypic peptides only)
[36:31] Quantification
[36:31] Precursors with monitored PTMs at 1% FDR: 3194 out of 33701 considered
[36:31] Unmodified precursors with monitored PTM sites at 1% FDR: 16906
[36:31] Precursors with PTMs localised (when required) with > 90% confidence: 3133 out of 3194

[36:32] Cross-run analysis
[36:32] Reading quantification information: 6 files
[36:35] Quantifying peptides
[38:04] Quantification parameters: 0.347465, 0.0014769, 0.00152104, 0.0130349, 0.012885, 0.0130015, 0.235923, 0.188926, 0.178413, 0.0151131, 0.0411532, 0.0245659, 0.252874, 0.0509697, 0.0608406, 0.0114525
[38:24] Quantifying proteins
[38:25] Calculating q-values for protein and gene groups
[38:25] Calculating global q-values for protein and gene groups
[38:25] Protein groups with global q-value <= 0.01: 20665
[38:30] Compressed report saved to run_output_Astral/diann_1.9.1_default/report.parquet. Use R 'arrow' or Python 'PyArrow' package to process
[38:30] Writing report
[38:52] Report saved to run_output_Astral/diann_1.9.1_default/report.tsv.
[38:52] Stats report saved to run_output_Astral/diann_1.9.1_default/report.stats.tsv

