
DIA-NN 2.3.0 Academia  (Data-Independent Acquisition by Neural Networks)
Compiled on Sep 26 2025 08:30:52
Current date and time: Mon Nov  3 14:03:28 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\bad_diann_settings\report.parquet --qvalue 0.1 --matrices --min-corr 2.0 --time-corr-only --extracted-ms1 --min-cal 500 --min-class 1000 --pre-filter --out-lib D:\Proteobench_manuscript_data\run_output_Astral\bad_diann_settings\output-lib.parquet --gen-spec-lib --predictor --fasta D:\Proteobench_manuscript_data\ProteoBenchFASTA_DDAQuantification.fasta --fasta-search --met-excision --min-pep-len 7 --max-pep-len 30 --min-pr-mz 700 --max-pr-mz 1000 --min-pr-charge 2 --max-pr-charge 3 --min-fr-mz 400 --max-fr-mz 1000 --cut R* --missed-cleavages 5 --var-mods 1 --mass-acc 40 --mass-acc-ms1 40 --mass-acc-cal 40 --fast-ml --rt-profiling 

Thread number set to 24
Output will be filtered at 0.1 FDR
Precursor/protein x samples expression level matrices will be saved along with the main report
Only peak groups with correlation-based score exceeding 2 will be considered; will be deactivated for the second MBR pass
DIA-NN will disable machine learning during the calibration stage to save memory, this may lead to somewhat lower identification numbers; will be deactivated for the second MBR pass
Ultra-fast mode will be used, this may worsen the identification performance; will be deactivated for the second MBR pass
Minimum number of precursors used for calibration set to 500
Minimum number of precursors used for linear classifier training set to 1000
DIA-NN will pre-filter candidate precursors
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
N-terminal methionine excision enabled
Min peptide length set to 7
Max peptide length set to 30
Min precursor m/z set to 700
Max precursor m/z set to 1000
Min precursor charge set to 2
Max precursor charge set to 3
Min fragment m/z set to 400
Max fragment m/z set to 1000
In silico digest will involve cuts at R*
Maximum number of missed cleavages set to 5
Maximum number of variable modifications set to 1
Calibration mass accuracy set to 4e-05
Fast neural network mode will be used
The spectral library (if generated) will retain the original spectra but will include empirically-aligned RTs
Mass accuracy will be fixed to 4e-05 (MS2) and 4e-05 (MS1)
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
WARNING: protein inference will only be performed for precursors identified with global q-value <= 0.05 or q-value <= 0.05.
WARNING: it is strongly recommended to set the q-value threshold to 5% or lower when generating an empirical library.

6 files will be processed
[0:00] Loading FASTA D:\Proteobench_manuscript_data\ProteoBenchFASTA_DDAQuantification.fasta
[0:01] Processing FASTA
[0:02] Assembling elution groups
[0:03] 707236 precursors generated
[0:03] Protein names missing for some isoforms
[0:03] Gene names missing for some isoforms
[0:03] Library contains 30774 proteins, and 0 genes
[0:04] [0:05] [2:17] [2:38] [2:39] [2:39] Saving the library to D:\Proteobench_manuscript_data\run_output_Astral\bad_diann_settings\output-lib.predicted.speclib
[2:40] Initialising library
[2:42] Loading spectral library D:\Proteobench_manuscript_data\run_output_Astral\bad_diann_settings\output-lib.predicted.speclib
[2:42] Library annotated with sequence database(s): D:\Proteobench_manuscript_data\ProteoBenchFASTA_DDAQuantification.fasta
[2:42] Spectral library loaded: 30911 protein isoforms, 33552 protein groups and 707236 precursors in 707236 elution groups.
[2:42] Loading protein annotations from FASTA D:\Proteobench_manuscript_data\ProteoBenchFASTA_DDAQuantification.fasta
[2:43] Annotating library proteins with information from the FASTA database
[2:43] Protein names missing for some isoforms
[2:43] Gene names missing for some isoforms
[2:43] Library contains 30774 proteins, and 0 genes
[2:43] Initialising library
WARNING: it is strongly recommended to enable MBR when analysing with a large library, if this is a quantitative analysis

[2:45] File #1/6
[2:45] Loading run D:\Proteobench_manuscript_data\Astral_raw\LFQ_Astral_DIA_15min_50ng_Condition_A_REP1.raw
[3:37] Analysing MS1 spectra
[3:37] Analysing MS2 spectra
[3:50] Integrating MS1 spectra
[3:51] Integrating MS2 spectra
[3:57] Pre-processing...
[3:57] 2931 MS1 and 293271 MS2 scans in 977 (inferred) and 977 (encoded) cycles, 663079 precursors in range
[3:57] Calibrating with mass accuracies 40 (MS1), 40 (MS2)
[4:00] RT window set to 1.56179
[4:00] Peak width: 2.636
[4:00] Scan window radius set to 5
[4:00] Recommended MS1 mass accuracy setting: 3 ppm
[4:12] Main search
[4:21] Removing low confidence identifications
[4:22] Removing interfering precursors
[4:22] Training neural networks on 7510 target and 4720 decoy PSMs
[4:22] IDs at 0.01 FDR: 2746
[4:22] Number of IDs at 0.01 FDR: 2805
[4:22] Calculating protein q-values
[4:23] Number of genes identified at 1% FDR: 0 (precursor-level), 0 (protein-level) (inference performed using proteotypic peptides only)
[4:23] Quantification
[4:23] Quantification information saved to D:\Proteobench_manuscript_data\Astral_raw\LFQ_Astral_DIA_15min_50ng_Condition_A_REP1.raw.quant

[4:23] File #2/6
[4:23] Loading run D:\Proteobench_manuscript_data\Astral_raw\LFQ_Astral_DIA_15min_50ng_Condition_A_REP2.raw
[5:14] Analysing MS1 spectra
[5:14] Analysing MS2 spectra
[5:27] Integrating MS1 spectra
[5:28] Integrating MS2 spectra
[5:34] Pre-processing...
[5:35] 2933 MS1 and 293433 MS2 scans in 978 (inferred) and 978 (encoded) cycles, 663079 precursors in range
[5:35] Calibrating with mass accuracies 40 (MS1), 40 (MS2)
[5:38] RT window set to 1.37255
[5:38] Recommended MS1 mass accuracy setting: 4.4 ppm
[5:51] Main search
[6:01] Removing low confidence identifications
[6:02] Removing interfering precursors
[6:02] Training neural networks on 7237 target and 4689 decoy PSMs
[6:02] IDs at 0.01 FDR: 2723
[6:03] Number of IDs at 0.01 FDR: 2765
[6:03] Calculating protein q-values
[6:03] Number of genes identified at 1% FDR: 0 (precursor-level), 0 (protein-level) (inference performed using proteotypic peptides only)
[6:03] Quantification
[6:04] Quantification information saved to D:\Proteobench_manuscript_data\Astral_raw\LFQ_Astral_DIA_15min_50ng_Condition_A_REP2.raw.quant

[6:04] File #3/6
[6:04] Loading run D:\Proteobench_manuscript_data\Astral_raw\LFQ_Astral_DIA_15min_50ng_Condition_A_REP3.raw
[6:56] Analysing MS1 spectra
[6:56] Analysing MS2 spectra
[7:10] Integrating MS1 spectra
[7:10] Integrating MS2 spectra
[7:17] Pre-processing...
[7:18] 2932 MS1 and 293358 MS2 scans in 978 (inferred) and 978 (encoded) cycles, 663079 precursors in range
[7:18] Calibrating with mass accuracies 40 (MS1), 40 (MS2)
[7:22] RT window set to 1.3399
[7:22] Recommended MS1 mass accuracy setting: 3 ppm
[7:43] Main search
[7:54] Removing low confidence identifications
[7:55] Removing interfering precursors
[7:55] Training neural networks on 6188 target and 4027 decoy PSMs
[7:56] IDs at 0.01 FDR: 2409
[7:57] Number of IDs at 0.01 FDR: 2460
[7:57] Calculating protein q-values
[7:57] Number of genes identified at 1% FDR: 0 (precursor-level), 0 (protein-level) (inference performed using proteotypic peptides only)
[7:57] Quantification
[7:58] Quantification information saved to D:\Proteobench_manuscript_data\Astral_raw\LFQ_Astral_DIA_15min_50ng_Condition_A_REP3.raw.quant

[7:59] File #4/6
[7:59] Loading run D:\Proteobench_manuscript_data\Astral_raw\LFQ_Astral_DIA_15min_50ng_Condition_B_REP1.raw
[8:57] Analysing MS1 spectra
[8:57] Analysing MS2 spectra
[9:11] Integrating MS1 spectra
[9:12] Integrating MS2 spectra
[9:18] Pre-processing...
[9:19] 2933 MS1 and 293382 MS2 scans in 978 (inferred) and 978 (encoded) cycles, 663079 precursors in range
[9:19] Calibrating with mass accuracies 40 (MS1), 40 (MS2)
[9:22] RT window set to 1.35413
[9:22] Recommended MS1 mass accuracy setting: 3.3 ppm
[9:33] Main search
[9:43] Removing low confidence identifications
[9:43] Removing interfering precursors
[9:43] Training neural networks on 6562 target and 4218 decoy PSMs
[9:44] IDs at 0.01 FDR: 2654
[9:44] Number of IDs at 0.01 FDR: 2691
[9:44] Calculating protein q-values
[9:44] Number of genes identified at 1% FDR: 0 (precursor-level), 0 (protein-level) (inference performed using proteotypic peptides only)
[9:44] Quantification
[9:45] Quantification information saved to D:\Proteobench_manuscript_data\Astral_raw\LFQ_Astral_DIA_15min_50ng_Condition_B_REP1.raw.quant

[9:45] File #5/6
[9:45] Loading run D:\Proteobench_manuscript_data\Astral_raw\LFQ_Astral_DIA_15min_50ng_Condition_B_REP2.raw
[10:40] Analysing MS1 spectra
[10:40] Analysing MS2 spectra
[10:53] Integrating MS1 spectra
[10:54] Integrating MS2 spectra
[11:00] Pre-processing...
[11:00] 2933 MS1 and 293330 MS2 scans in 978 (inferred) and 978 (encoded) cycles, 663079 precursors in range
[11:00] Calibrating with mass accuracies 40 (MS1), 40 (MS2)
[11:03] RT window set to 1.43574
[11:03] Recommended MS1 mass accuracy setting: 3 ppm
[11:14] Main search
[11:24] Removing low confidence identifications
[11:24] Removing interfering precursors
[11:24] Training neural networks on 7099 target and 4401 decoy PSMs
[11:25] IDs at 0.01 FDR: 2860
[11:25] Number of IDs at 0.01 FDR: 2903
[11:25] Calculating protein q-values
[11:25] Number of genes identified at 1% FDR: 0 (precursor-level), 0 (protein-level) (inference performed using proteotypic peptides only)
[11:25] Quantification
[11:26] Quantification information saved to D:\Proteobench_manuscript_data\Astral_raw\LFQ_Astral_DIA_15min_50ng_Condition_B_REP2.raw.quant

[11:26] File #6/6
[11:26] Loading run D:\Proteobench_manuscript_data\Astral_raw\LFQ_Astral_DIA_15min_50ng_Condition_B_REP3.raw
[12:19] Analysing MS1 spectra
[12:19] Analysing MS2 spectra
[12:32] Integrating MS1 spectra
[12:32] Integrating MS2 spectra
[12:39] Pre-processing...
[12:41] 2934 MS1 and 293446 MS2 scans in 978 (inferred) and 978 (encoded) cycles, 663079 precursors in range
[12:41] Calibrating with mass accuracies 40 (MS1), 40 (MS2)
[12:45] RT window set to 1.23028
[12:45] Recommended MS1 mass accuracy setting: 3.3 ppm
[13:01] Main search
[13:13] Removing low confidence identifications
[13:14] Removing interfering precursors
[13:15] Training neural networks on 7325 target and 4616 decoy PSMs
[13:15] IDs at 0.01 FDR: 2906
[13:15] Number of IDs at 0.01 FDR: 2977
[13:15] Calculating protein q-values
[13:15] Number of genes identified at 1% FDR: 0 (precursor-level), 0 (protein-level) (inference performed using proteotypic peptides only)
[13:16] Quantification
[13:17] Quantification information saved to D:\Proteobench_manuscript_data\Astral_raw\LFQ_Astral_DIA_15min_50ng_Condition_B_REP3.raw.quant

[13:17] Cross-run analysis
[13:17] Reading quantification information: 6 files
[13:20] Quantifying peptides
[13:22] Quantification parameters: 0.386442, 0.00819672, 0.0029481, 0.103649, 0.146575, 0.18274, 0.490514, 0.0818286, 0.165759, 0.134286, 0.0504399, 0.059024, 0.227487, 0.0505485, 0.0594663, 0.0119926
[13:23] Assembling protein groups
[13:23] Quantifying proteins
[13:23] Calculating q-values for protein and gene groups
[13:23] Calculating global q-values for protein and gene groups
[13:23] Protein groups with global q-value <= 0.01: 2604
[13:23] Compressed report saved to D:\Proteobench_manuscript_data\run_output_Astral\bad_diann_settings\report.parquet. Use R 'arrow' or Python 'PyArrow' package to process
[13:23] Saving precursor levels matrix
[13:23] Precursor levels matrix (1% precursor and protein group FDR) saved to D:\Proteobench_manuscript_data\run_output_Astral\bad_diann_settings\report.pr_matrix.tsv.
[13:23] Saving protein group levels matrix
[13:23] Protein groups matrix saved to D:\Proteobench_manuscript_data\run_output_Astral\bad_diann_settings\report.pg_matrix.tsv.
[13:23] Saving gene group levels matrix
[13:23] Gene groups matrix saved to D:\Proteobench_manuscript_data\run_output_Astral\bad_diann_settings\report.gg_matrix.tsv.
[13:23] Saving unique genes levels matrix
[13:23] Unique genes matrix saved to D:\Proteobench_manuscript_data\run_output_Astral\bad_diann_settings\report.unique_genes_matrix.tsv.
[13:23] Manifest saved to D:\Proteobench_manuscript_data\run_output_Astral\bad_diann_settings\report.manifest.txt
[13:23] Stats report saved to D:\Proteobench_manuscript_data\run_output_Astral\bad_diann_settings\report.stats.tsv
[13:23] Generating spectral library:
[13:23] 5257 target and 525 decoy precursors saved
[13:23] Spectral library saved to D:\Proteobench_manuscript_data\run_output_Astral\bad_diann_settings\output-lib.parquet

