
DIA-NN 2.2.0 Academia  (Data-Independent Acquisition by Neural Networks)
Compiled on May 29 2025 21:29:29
Current date and time: Thu Jul 17 17:12:21 2025
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\Astral_raw\LFQ_Astral_DIA_15min_50ng_Condition_A_REP1.raw  --f D:\Proteobench_manuscript_data\Astral_raw\LFQ_Astral_DIA_15min_50ng_Condition_A_REP2.raw  --f D:\Proteobench_manuscript_data\Astral_raw\LFQ_Astral_DIA_15min_50ng_Condition_A_REP3.raw  --f D:\Proteobench_manuscript_data\Astral_raw\LFQ_Astral_DIA_15min_50ng_Condition_B_REP1.raw  --f D:\Proteobench_manuscript_data\Astral_raw\LFQ_Astral_DIA_15min_50ng_Condition_B_REP2.raw  --f D:\Proteobench_manuscript_data\Astral_raw\LFQ_Astral_DIA_15min_50ng_Condition_B_REP3.raw  --lib  --threads 24 --verbose 1 --out D:\Proteobench_manuscript_data\run_output_Astral\diann-2.2.0-nombr\report.parquet --qvalue 0.01 --matrices --out-lib C:\DIA-NN\2.2.0\report-lib.parquet --gen-spec-lib --predictor --fasta D:\Proteobench_manuscript_data\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 5 --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 --rt-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
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 5
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
The spectral library (if generated) will retain the original spectra but will include empirically-aligned RTs
DIA-NN will automatically optimise the mass accuracy for the first run of the experiment, use this mode for preliminary analyses only
WARNING: incorrect settings, the in silico-predicted library must be generated in a separate pipeline step and then used to process the raw data, now without activating FASTA digest
The following variable modifications will be localised: UniMod:35 UniMod:1 

6 files will be processed
[0:00] Loading FASTA D:\Proteobench_manuscript_data\ProteoBenchFASTA_DDAQuantification.fasta
[0:03] Processing FASTA
[0:06] Assembling elution groups
[0:14] 5116700 precursors generated
[0:15] Protein names missing for some isoforms
[0:15] Gene names missing for some isoforms
[0:15] Library contains 31685 proteins, and 0 genes
[0:19] [0:35] [8:10] [9:12] [9:16] [9:17] Saving the library to C:\DIA-NN\2.2.0\report-lib.predicted.speclib
[9:21] Initialising library
[9:30] Loading spectral library C:\DIA-NN\2.2.0\report-lib.predicted.speclib
[9:33] Library annotated with sequence database(s): D:\Proteobench_manuscript_data\ProteoBenchFASTA_DDAQuantification.fasta
[9:34] Spectral library loaded: 31837 protein isoforms, 51765 protein groups and 5116700 precursors in 2716671 elution groups.
[9:34] Loading protein annotations from FASTA D:\Proteobench_manuscript_data\ProteoBenchFASTA_DDAQuantification.fasta
[9:34] Annotating library proteins with information from the FASTA database
[9:34] Protein names missing for some isoforms
[9:34] Gene names missing for some isoforms
[9:34] Library contains 31685 proteins, and 0 genes
[9:36] Initialising library
WARNING: it is strongly recommended to enable MBR when analysing with a large library, if this is a quantitative analysis

[9:45] File #1/6
[9:45] Loading run D:\Proteobench_manuscript_data\Astral_raw\LFQ_Astral_DIA_15min_50ng_Condition_A_REP1.raw
[9:56] Pre-processing...
[9:58] 2931 MS1 and 293271 MS2 scans in 977 (inferred) and 977 (encoded) cycles, 5020863 precursors in range
[9:58] Calibrating with mass accuracies 21 (MS1), 25 (MS2)
[10:17] RT window set to 1.37503
[10:17] Peak width: 2.836
[10:17] Scan window radius set to 6
[10:17] Recommended MS1 mass accuracy setting: 2.8 ppm
[10:40] Optimised mass accuracy: 6 ppm
[10:47] Searching decoys
[11:29] Main search
[12:52] Removing low confidence identifications
[13:04] Removing interfering precursors
[13:10] Training neural networks on 207378 target and 130901 decoy PSMs
[15:31] Training neural networks on 207378 target and 129446 decoy PSMs
[17:58] Number of IDs at 0.01 FDR: 103247
[17:59] Precursors at 1% peptidoform FDR: 100642
[17:59] Calculating protein q-values
[18:00] Number of genes identified at 1% FDR: 0 (precursor-level), 0 (protein-level) (inference performed using proteotypic peptides only)
[18:00] Quantification
[18:02] Precursors with scored PTMs at 1% FDR: 3407 out of 3589 considered
[18:02] Precursors with all scored PTM sites unoccupied at 1% FDR: 97235
[18:02] Precursors with PTMs localised (when required) with > 90% confidence: 3302 out of 3407
[18:03] Quantification information saved to D:\Proteobench_manuscript_data\Astral_raw\LFQ_Astral_DIA_15min_50ng_Condition_A_REP1.raw.quant

[18:03] File #2/6
[18:03] Loading run D:\Proteobench_manuscript_data\Astral_raw\LFQ_Astral_DIA_15min_50ng_Condition_A_REP2.raw
[19:04] Pre-processing...
[19:06] 2933 MS1 and 293433 MS2 scans in 978 (inferred) and 978 (encoded) cycles, 5020863 precursors in range
[19:07] Calibrating with mass accuracies 21 (MS1), 25 (MS2)
[19:33] RT window set to 1.23136
[19:34] Recommended MS1 mass accuracy setting: 2.8 ppm
[19:40] Searching decoys
[20:34] Main search
[22:16] Removing low confidence identifications
[22:35] Removing interfering precursors
[22:46] Training neural networks on 219132 target and 137947 decoy PSMs
[25:32] Training neural networks on 219132 target and 136917 decoy PSMs
[28:10] Number of IDs at 0.01 FDR: 106335
[28:11] Precursors at 1% peptidoform FDR: 103046
[28:12] Calculating protein q-values
[28:12] Number of genes identified at 1% FDR: 0 (precursor-level), 0 (protein-level) (inference performed using proteotypic peptides only)
[28:13] Quantification
[28:14] Precursors with scored PTMs at 1% FDR: 3550 out of 3862 considered
[28:14] Precursors with all scored PTM sites unoccupied at 1% FDR: 99496
[28:14] Precursors with PTMs localised (when required) with > 90% confidence: 3465 out of 3550
[28:15] Quantification information saved to D:\Proteobench_manuscript_data\Astral_raw\LFQ_Astral_DIA_15min_50ng_Condition_A_REP2.raw.quant

[28:16] File #3/6
[28:16] Loading run D:\Proteobench_manuscript_data\Astral_raw\LFQ_Astral_DIA_15min_50ng_Condition_A_REP3.raw
[29:15] Pre-processing...
[29:17] 2932 MS1 and 293358 MS2 scans in 978 (inferred) and 978 (encoded) cycles, 5020863 precursors in range
[29:17] Calibrating with mass accuracies 21 (MS1), 25 (MS2)
[29:44] RT window set to 1.16852
[29:44] Recommended MS1 mass accuracy setting: 2.7 ppm
[29:51] Searching decoys
[30:42] Main search
[32:20] Removing low confidence identifications
[32:39] Removing interfering precursors
[32:50] Training neural networks on 216845 target and 135950 decoy PSMs
[35:26] Training neural networks on 216845 target and 135189 decoy PSMs
[37:53] Number of IDs at 0.01 FDR: 105834
[37:54] Precursors at 1% peptidoform FDR: 103073
[37:55] Calculating protein q-values
[37:55] Number of genes identified at 1% FDR: 0 (precursor-level), 0 (protein-level) (inference performed using proteotypic peptides only)
[37:56] Quantification
[37:57] Precursors with scored PTMs at 1% FDR: 3575 out of 3792 considered
[37:57] Precursors with all scored PTM sites unoccupied at 1% FDR: 99498
[37:57] Precursors with PTMs localised (when required) with > 90% confidence: 3482 out of 3575
[37:58] Quantification information saved to D:\Proteobench_manuscript_data\Astral_raw\LFQ_Astral_DIA_15min_50ng_Condition_A_REP3.raw.quant

[37:59] File #4/6
[37:59] Loading run D:\Proteobench_manuscript_data\Astral_raw\LFQ_Astral_DIA_15min_50ng_Condition_B_REP1.raw
[39:02] Pre-processing...
[39:04] 2933 MS1 and 293382 MS2 scans in 978 (inferred) and 978 (encoded) cycles, 5020863 precursors in range
[39:05] Calibrating with mass accuracies 21 (MS1), 25 (MS2)
[39:32] RT window set to 1.41965
[39:32] Recommended MS1 mass accuracy setting: 2.9 ppm
[39:40] Searching decoys
[40:38] Main search
[42:29] Removing low confidence identifications
[42:47] Removing interfering precursors
[42:58] Training neural networks on 221345 target and 141460 decoy PSMs
[45:32] Training neural networks on 221345 target and 139625 decoy PSMs
[48:00] Number of IDs at 0.01 FDR: 106379
[48:01] Precursors at 1% peptidoform FDR: 103559
[48:02] Calculating protein q-values
[48:02] Number of genes identified at 1% FDR: 0 (precursor-level), 0 (protein-level) (inference performed using proteotypic peptides only)
[48:02] Quantification
[48:04] Precursors with scored PTMs at 1% FDR: 4178 out of 4471 considered
[48:04] Precursors with all scored PTM sites unoccupied at 1% FDR: 99381
[48:04] Precursors with PTMs localised (when required) with > 90% confidence: 4059 out of 4178
[48:05] Quantification information saved to D:\Proteobench_manuscript_data\Astral_raw\LFQ_Astral_DIA_15min_50ng_Condition_B_REP1.raw.quant

[48:06] File #5/6
[48:06] Loading run D:\Proteobench_manuscript_data\Astral_raw\LFQ_Astral_DIA_15min_50ng_Condition_B_REP2.raw
[49:10] Pre-processing...
[49:12] 2933 MS1 and 293330 MS2 scans in 978 (inferred) and 978 (encoded) cycles, 5020863 precursors in range
[49:13] Calibrating with mass accuracies 21 (MS1), 25 (MS2)
[49:39] RT window set to 1.23393
[49:39] Recommended MS1 mass accuracy setting: 2.8 ppm
[49:47] Searching decoys
[50:37] Main search
[52:13] Removing low confidence identifications
[52:32] Removing interfering precursors
[52:42] Training neural networks on 219236 target and 139403 decoy PSMs
[55:17] Training neural networks on 219236 target and 138035 decoy PSMs
[57:43] Number of IDs at 0.01 FDR: 106358
[57:44] Precursors at 1% peptidoform FDR: 103528
[57:45] Calculating protein q-values
[57:45] Number of genes identified at 1% FDR: 0 (precursor-level), 0 (protein-level) (inference performed using proteotypic peptides only)
[57:45] Quantification
[57:47] Precursors with scored PTMs at 1% FDR: 4158 out of 4467 considered
[57:47] Precursors with all scored PTM sites unoccupied at 1% FDR: 99370
[57:47] Precursors with PTMs localised (when required) with > 90% confidence: 4051 out of 4158
[57:48] Quantification information saved to D:\Proteobench_manuscript_data\Astral_raw\LFQ_Astral_DIA_15min_50ng_Condition_B_REP2.raw.quant

[57:49] File #6/6
[57:49] Loading run D:\Proteobench_manuscript_data\Astral_raw\LFQ_Astral_DIA_15min_50ng_Condition_B_REP3.raw
[58:53] Pre-processing...
[58:54] 2934 MS1 and 293446 MS2 scans in 978 (inferred) and 978 (encoded) cycles, 5020863 precursors in range
[58:55] Calibrating with mass accuracies 21 (MS1), 25 (MS2)
[59:22] RT window set to 1.1542
[59:22] Recommended MS1 mass accuracy setting: 2.6 ppm
[59:28] Searching decoys
[60:16] Main search
[61:50] Removing low confidence identifications
[62:09] Removing interfering precursors
[62:19] Training neural networks on 217786 target and 136998 decoy PSMs
[64:51] Training neural networks on 217786 target and 136088 decoy PSMs
[67:15] Number of IDs at 0.01 FDR: 106137
[67:16] Precursors at 1% peptidoform FDR: 103217
[67:17] Calculating protein q-values
[67:18] Number of genes identified at 1% FDR: 0 (precursor-level), 0 (protein-level) (inference performed using proteotypic peptides only)
[67:18] Quantification
[67:19] Precursors with scored PTMs at 1% FDR: 4196 out of 4446 considered
[67:19] Precursors with all scored PTM sites unoccupied at 1% FDR: 99021
[67:19] Precursors with PTMs localised (when required) with > 90% confidence: 4080 out of 4196
[67:21] Quantification information saved to D:\Proteobench_manuscript_data\Astral_raw\LFQ_Astral_DIA_15min_50ng_Condition_B_REP3.raw.quant

[67:21] Cross-run analysis
[67:21] Reading quantification information: 6 files
[67:42] Quantifying peptides
[71:27] Quantification parameters: 0.356674, 0.00142146, 0.00158556, 0.0120816, 0.0120182, 0.012055, 0.187414, 0.242634, 0.185855, 0.0138728, 0.0326617, 0.0147896, 0.369301, 0.0531569, 0.0789269, 0.0115076
[73:06] Assembling protein groups
[73:09] Quantifying proteins
[73:09] Calculating q-values for protein and gene groups
[73:11] Calculating global q-values for protein and gene groups
[73:12] Protein groups with global q-value <= 0.01: 11589
[73:17] Compressed report saved to D:\Proteobench_manuscript_data\run_output_Astral\diann-2.2.0-nombr\report.parquet. Use R 'arrow' or Python 'PyArrow' package to process
[73:17] Site report saved to D:\Proteobench_manuscript_data\run_output_Astral\diann-2.2.0-nombr\report.site_report.parquet
[73:17] Saving precursor levels matrix
[73:17] Precursor levels matrix (1% precursor and protein group FDR) saved to D:\Proteobench_manuscript_data\run_output_Astral\diann-2.2.0-nombr\report.pr_matrix.tsv.
[73:17] Saving protein group levels matrix
[73:17] Protein groups matrix saved to D:\Proteobench_manuscript_data\run_output_Astral\diann-2.2.0-nombr\report.pg_matrix.tsv.
[73:17] Saving gene group levels matrix
[73:17] Gene groups matrix saved to D:\Proteobench_manuscript_data\run_output_Astral\diann-2.2.0-nombr\report.gg_matrix.tsv.
[73:17] Saving unique genes levels matrix
[73:18] Unique genes matrix saved to D:\Proteobench_manuscript_data\run_output_Astral\diann-2.2.0-nombr\report.unique_genes_matrix.tsv.
[73:18] Manifest saved to D:\Proteobench_manuscript_data\run_output_Astral\diann-2.2.0-nombr\report.manifest.txt
[73:18] Stats report saved to D:\Proteobench_manuscript_data\run_output_Astral\diann-2.2.0-nombr\report.stats.tsv
[73:18] Generating spectral library:
[73:21] 136827 target and 1389 decoy precursors saved
[73:21] Spectral library saved to C:\DIA-NN\2.2.0\report-lib.parquet

