
DIA-NN 2.6.0 Enterprise  (Data-Independent Acquisition by Neural Networks)
Compiled on Jun  8 2026 16:03:38
Current date and time: Wed Jun 10 01:34:12 2026
CPU: AuthenticAMD AMD Ryzen Threadripper 7980X 64-Cores
SIMD instructions: AVX AVX2 AVX512CD AVX512F FMA SSE4.1 SSE4.2 SSE4a 
Logical CPU cores: 128
205Gb out of 255Gb RAM is free
diann.exe --f C:\Raw\LFQ_Astral_DDA\LFQ_Astral_DDA_15min_50ng_Condition_A_REP1.raw --f C:\Raw\LFQ_Astral_DDA\LFQ_Astral_DDA_15min_50ng_Condition_A_REP2.raw --f C:\Raw\LFQ_Astral_DDA\LFQ_Astral_DDA_15min_50ng_Condition_A_REP3.raw --f C:\Raw\LFQ_Astral_DDA\LFQ_Astral_DDA_15min_50ng_Condition_B_REP1.raw --f C:\Raw\LFQ_Astral_DDA\LFQ_Astral_DDA_15min_50ng_Condition_B_REP2.raw --f C:\Raw\LFQ_Astral_DDA\LFQ_Astral_DDA_15min_50ng_Condition_B_REP3.raw --lib C:\Raw\LFQ_Astral_DDA\proteobench.predicted.speclib --threads 64 --verbose 1 --out C:/Out/q-adda.parquet --qvalue 0.05 --matrices --rt-window-mul 1.7 --rt-window-factor 100 --temp C:/Out/Temp --out-lib C:/Out/q-adda-lib.parquet --gen-spec-lib --fasta C:\Raw\LFQ_Astral_DDA\ProteoBenchFASTA_MixedSpecies_HYE.fasta --met-excision --min-pep-len 7 --max-pep-len 30 --min-pr-mz 400 --max-pr-mz 1000 --min-pr-charge 2 --max-pr-charge 3 --min-fr-mz 200 --max-fr-mz 1800 --cut K*,R* --missed-cleavages 1 --unimod4 --var-mods 1 --window 1 --mass-acc 10 --mass-acc-ms1 4 --mass-acc-cal 20 --reanalyse --rt-profiling --pg-level 0 --high-acc --auto-aff --kb --dda 

Thread number set to 64
Output will be filtered at 0.05 FDR
Precursor/protein x samples expression level matrices will be saved along with the main report
RT window multiplier will be set to 1.7
RT window factor set to 100
A spectral library will be generated
N-terminal methionine excision enabled
Min peptide length set to 7
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 2
Max precursor charge set to 3
Min fragment m/z set to 200
Max fragment m/z set to 1800
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
Scan window radius set to 1
Calibration mass accuracy set to 2e-05
MBR enabled; .quant files will only be saved to disk during the first pass
The spectral library (if generated) will retain the original spectra but will include empirically-aligned RTs
Implicit protein grouping: isoform IDs; this determines which peptides are considered 'proteotypic' and thus affects protein FDR calculation
High accuracy quantification mode enabled
DIA-NN will spread its threads over all CPUs in the system
Knowledge-based scoring enabled
All runs will be analysed as DDA runs
Mass accuracy will be fixed to 1e-05 (MS2) and 4e-06 (MS1)
WARNING: QuantUMS cannot be used on DDA data, disabled

6 files will be processed
[0:00] Loading spectral library C:\Raw\LFQ_Astral_DDA\proteobench.predicted.speclib
[0:07] Library annotated with sequence database(s): C:\Raw\LFQ_Astral_DDA\ProteoBenchFASTA_MixedSpecies_HYE.fasta
[0:08] Spectral library loaded: 31833 protein isoforms, 43791 protein groups and 8516312 precursors in 4330170 elution groups (targets and decoys).
[0:08] Loading protein annotations from FASTA C:\Raw\LFQ_Astral_DDA\ProteoBenchFASTA_MixedSpecies_HYE.fasta
[0:08] Annotating library proteins with information from the FASTA database
[0:08] Protein names missing for some isoforms
[0:08] Gene names missing for some isoforms
[0:08] Library contains 31681 proteins, and 0 genes
WARNING: no gene information in the FASTA or library: consider using --ids-to-names
[0:13] Initialising library

First pass: generating a spectral library from DIA data

[0:26] File #1/6
[0:26] Loading run C:\Raw\LFQ_Astral_DDA\LFQ_Astral_DDA_15min_50ng_Condition_A_REP1.raw
[0:35] Pre-processing...
[0:36] 3686 MS1 and 123545 MS2 scans in 1771 (inferred) and 890 (encoded) cycles
[0:37] Calibrating with mass accuracies 20 (MS1), 20 (MS2)
[1:08] RT window set to 0.740678
[1:08] Recommended MS1 mass accuracy setting: 3.1 ppm
[1:14] Main search
[2:07] Removing low confidence identifications
[2:22] Removing interfering precursors
[2:30] Training neural networks on 371890 target and 304768 decoy PSMs
[3:10] Number of IDs at 0.01 FDR: 86099
[3:10] Calculating protein q-values
[3:11] Number of protein isoforms identified at 1% FDR: 10007 (precursor-level), 9316 (protein-level) (inference performed using proteotypic peptides only)
[3:11] Quantification
[3:13] Quantification information saved to C:/Out/Temp/C__Raw_LFQ_Astral_DDA_LFQ_Astral_DDA_15min_50ng_Condition_A_REP1_raw.quant

[3:13] File #2/6
[3:13] Loading run C:\Raw\LFQ_Astral_DDA\LFQ_Astral_DDA_15min_50ng_Condition_A_REP2.raw
[3:22] Pre-processing...
[3:23] 3680 MS1 and 123627 MS2 scans in 1769 (inferred) and 892 (encoded) cycles
[3:24] Calibrating with mass accuracies 20 (MS1), 20 (MS2)
[3:55] RT window set to 0.658692
[3:55] Recommended MS1 mass accuracy setting: 2.8 ppm
[4:01] Main search
[4:49] Removing low confidence identifications
[5:02] Removing interfering precursors
[5:11] Training neural networks on 364004 target and 295488 decoy PSMs
[5:50] Number of IDs at 0.01 FDR: 84501
[5:50] Calculating protein q-values
[5:50] Number of protein isoforms identified at 1% FDR: 9902 (precursor-level), 9162 (protein-level) (inference performed using proteotypic peptides only)
[5:50] Quantification
[5:52] Quantification information saved to C:/Out/Temp/C__Raw_LFQ_Astral_DDA_LFQ_Astral_DDA_15min_50ng_Condition_A_REP2_raw.quant

[5:52] File #3/6
[5:52] Loading run C:\Raw\LFQ_Astral_DDA\LFQ_Astral_DDA_15min_50ng_Condition_A_REP3.raw
[6:01] Pre-processing...
[6:01] 3691 MS1 and 123540 MS2 scans in 1759 (inferred) and 886 (encoded) cycles
[6:02] Calibrating with mass accuracies 20 (MS1), 20 (MS2)
[6:33] RT window set to 0.691035
[6:33] Recommended MS1 mass accuracy setting: 3 ppm
[6:39] Main search
[7:29] Removing low confidence identifications
[7:43] Removing interfering precursors
[7:52] Training neural networks on 375809 target and 309541 decoy PSMs
[8:32] Number of IDs at 0.01 FDR: 84791
[8:32] Calculating protein q-values
[8:33] Number of protein isoforms identified at 1% FDR: 9811 (precursor-level), 9074 (protein-level) (inference performed using proteotypic peptides only)
[8:33] Quantification
[8:35] Quantification information saved to C:/Out/Temp/C__Raw_LFQ_Astral_DDA_LFQ_Astral_DDA_15min_50ng_Condition_A_REP3_raw.quant

[8:35] File #4/6
[8:35] Loading run C:\Raw\LFQ_Astral_DDA\LFQ_Astral_DDA_15min_50ng_Condition_B_REP1.raw
[8:43] Pre-processing...
[8:44] 3702 MS1 and 122692 MS2 scans in 1774 (inferred) and 898 (encoded) cycles
[8:45] Calibrating with mass accuracies 20 (MS1), 20 (MS2)
[9:16] RT window set to 0.717879
[9:17] Recommended MS1 mass accuracy setting: 3.1 ppm
[9:23] Main search
[10:15] Removing low confidence identifications
[10:30] Removing interfering precursors
[10:39] Training neural networks on 387379 target and 320817 decoy PSMs
[11:21] Number of IDs at 0.01 FDR: 88103
[11:21] Calculating protein q-values
[11:21] Number of protein isoforms identified at 1% FDR: 9737 (precursor-level), 9047 (protein-level) (inference performed using proteotypic peptides only)
[11:22] Quantification
[11:24] Quantification information saved to C:/Out/Temp/C__Raw_LFQ_Astral_DDA_LFQ_Astral_DDA_15min_50ng_Condition_B_REP1_raw.quant

[11:24] File #5/6
[11:24] Loading run C:\Raw\LFQ_Astral_DDA\LFQ_Astral_DDA_15min_50ng_Condition_B_REP2.raw
[11:32] Pre-processing...
[11:33] 3701 MS1 and 122590 MS2 scans in 1764 (inferred) and 903 (encoded) cycles
[11:34] Calibrating with mass accuracies 20 (MS1), 20 (MS2)
[12:05] RT window set to 0.676955
[12:05] Recommended MS1 mass accuracy setting: 2.9 ppm
[12:11] Main search
[12:59] Removing low confidence identifications
[13:13] Removing interfering precursors
[13:22] Training neural networks on 373296 target and 307454 decoy PSMs
[14:02] Number of IDs at 0.01 FDR: 87562
[14:02] Calculating protein q-values
[14:03] Number of protein isoforms identified at 1% FDR: 9823 (precursor-level), 9149 (protein-level) (inference performed using proteotypic peptides only)
[14:03] Quantification
[14:05] Quantification information saved to C:/Out/Temp/C__Raw_LFQ_Astral_DDA_LFQ_Astral_DDA_15min_50ng_Condition_B_REP2_raw.quant

[14:05] File #6/6
[14:05] Loading run C:\Raw\LFQ_Astral_DDA\LFQ_Astral_DDA_15min_50ng_Condition_B_REP3.raw
[14:14] Pre-processing...
[14:15] 3697 MS1 and 122653 MS2 scans in 1758 (inferred) and 895 (encoded) cycles
[14:16] Calibrating with mass accuracies 20 (MS1), 20 (MS2)
[14:47] RT window set to 0.671506
[14:47] Recommended MS1 mass accuracy setting: 3 ppm
[14:53] Main search
[15:43] Removing low confidence identifications
[15:58] Removing interfering precursors
[16:06] Training neural networks on 381567 target and 314867 decoy PSMs
[16:47] Number of IDs at 0.01 FDR: 87444
[16:47] Calculating protein q-values
[16:48] Number of protein isoforms identified at 1% FDR: 9749 (precursor-level), 9020 (protein-level) (inference performed using proteotypic peptides only)
[16:48] Quantification
[16:50] Quantification information saved to C:/Out/Temp/C__Raw_LFQ_Astral_DDA_LFQ_Astral_DDA_15min_50ng_Condition_B_REP3_raw.quant

[16:50] Cross-run analysis
[16:50] Reading quantification information: 6 files
[17:07] Target precursors at 1% global q-value: 122131
[17:08] Quantifying peptides
[17:44] Assembling protein groups
[17:46] Quantifying proteins
[17:46] Calculating q-values for protein and gene groups
[17:49] Calculating global q-values for protein and gene groups
[17:49] Protein groups with global q-value <= 0.01: 10933
[17:52] Compressed report saved to C:/Out/q-adda-first-pass.parquet. Use R 'arrow' or Python 'PyArrow' package to process
[17:52] Saving precursor levels matrix
[17:52] Precursor levels matrix (1% precursor and protein group FDR) saved to C:/Out/q-adda-first-pass.pr_matrix.tsv.
[17:52] Manifest saved to C:/Out/q-adda-first-pass.manifest.txt
[17:52] Stats report saved to C:/Out/q-adda-first-pass.stats.tsv
[17:52] Generating spectral library:
[17:55] 152119 target and 10954 decoy precursors saved
[17:55] Spectral library saved to C:/Out/q-adda-lib.parquet

[17:56] Loading spectral library C:/Out/q-adda-lib.parquet
[17:58] Spectral library loaded: 18168 protein isoforms, 18077 protein groups and 163072 precursors in 150408 elution groups (targets and decoys).
[17:58] Loading protein annotations from FASTA C:\Raw\LFQ_Astral_DDA\ProteoBenchFASTA_MixedSpecies_HYE.fasta
[17:58] Annotating library proteins with information from the FASTA database
[17:58] Gene names missing for some isoforms
[17:58] Library contains 18160 proteins, and 0 genes
WARNING: no gene information in the FASTA or library: consider using --ids-to-names
[17:58] Initialising library
[17:59] Saving the library to C:/Out/q-adda-lib.parquet.skyline.speclib


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

[17:59] File #1/6
[17:59] Loading run C:\Raw\LFQ_Astral_DDA\LFQ_Astral_DDA_15min_50ng_Condition_A_REP1.raw
[18:07] Pre-processing...
[18:08] 3686 MS1 and 123545 MS2 scans in 1771 (inferred) and 890 (encoded) cycles
[18:08] Calibrating with mass accuracies 20 (MS1), 20 (MS2)
[18:09] RT window set to 0.413582
[18:09] Recommended MS1 mass accuracy setting: 3.1 ppm
[18:09] Main search
[18:12] Removing low confidence identifications
[18:15] Removing interfering precursors
[18:16] Training neural networks on 133999 target and 66456 decoy PSMs
[18:29] Training neural networks on 92378 target and 41501 decoy PSMs
[18:33] Number of IDs at 0.01 FDR: 96561
[18:33] Calculating protein q-values
[18:34] Number of protein isoforms identified at 1% FDR: 9586 (precursor-level), 9961 (protein-level) (inference performed using proteotypic peptides only)
[18:34] Quantification

[18:35] File #2/6
[18:35] Loading run C:\Raw\LFQ_Astral_DDA\LFQ_Astral_DDA_15min_50ng_Condition_A_REP2.raw
[18:43] Pre-processing...
[18:43] 3680 MS1 and 123627 MS2 scans in 1769 (inferred) and 892 (encoded) cycles
[18:43] Calibrating with mass accuracies 20 (MS1), 20 (MS2)
[18:45] RT window set to 0.405938
[18:45] Recommended MS1 mass accuracy setting: 2.8 ppm
[18:45] Main search
[18:48] Removing low confidence identifications
[18:51] Removing interfering precursors
[18:52] Training neural networks on 134097 target and 67287 decoy PSMs
[19:04] Training neural networks on 93029 target and 42236 decoy PSMs
[19:09] Number of IDs at 0.01 FDR: 97488
[19:09] Calculating protein q-values
[19:09] Number of protein isoforms identified at 1% FDR: 9576 (precursor-level), 9864 (protein-level) (inference performed using proteotypic peptides only)
[19:09] Quantification

[19:10] File #3/6
[19:10] Loading run C:\Raw\LFQ_Astral_DDA\LFQ_Astral_DDA_15min_50ng_Condition_A_REP3.raw
[19:18] Pre-processing...
[19:19] 3691 MS1 and 123540 MS2 scans in 1759 (inferred) and 886 (encoded) cycles
[19:19] Calibrating with mass accuracies 20 (MS1), 20 (MS2)
[19:20] RT window set to 0.412377
[19:20] Recommended MS1 mass accuracy setting: 3 ppm
[19:20] Main search
[19:23] Removing low confidence identifications
[19:26] Removing interfering precursors
[19:27] Training neural networks on 134552 target and 66868 decoy PSMs
[19:39] Training neural networks on 92525 target and 41866 decoy PSMs
[19:44] Number of IDs at 0.01 FDR: 97316
[19:44] Calculating protein q-values
[19:44] Number of protein isoforms identified at 1% FDR: 9542 (precursor-level), 9793 (protein-level) (inference performed using proteotypic peptides only)
[19:44] Quantification

[19:45] File #4/6
[19:45] Loading run C:\Raw\LFQ_Astral_DDA\LFQ_Astral_DDA_15min_50ng_Condition_B_REP1.raw
[19:54] Pre-processing...
[19:54] 3702 MS1 and 122692 MS2 scans in 1774 (inferred) and 898 (encoded) cycles
[19:54] Calibrating with mass accuracies 20 (MS1), 20 (MS2)
[19:55] RT window set to 0.424141
[19:56] Recommended MS1 mass accuracy setting: 2.9 ppm
[19:56] Main search
[19:59] Removing low confidence identifications
[20:02] Removing interfering precursors
[20:03] Training neural networks on 136164 target and 67081 decoy PSMs
[20:15] Training neural networks on 93727 target and 42210 decoy PSMs
[20:20] Number of IDs at 0.01 FDR: 100559
[20:20] Calculating protein q-values
[20:20] Number of protein isoforms identified at 1% FDR: 9659 (precursor-level), 9765 (protein-level) (inference performed using proteotypic peptides only)
[20:20] Quantification

[20:21] File #5/6
[20:21] Loading run C:\Raw\LFQ_Astral_DDA\LFQ_Astral_DDA_15min_50ng_Condition_B_REP2.raw
[20:29] Pre-processing...
[20:29] 3701 MS1 and 122590 MS2 scans in 1764 (inferred) and 903 (encoded) cycles
[20:29] Calibrating with mass accuracies 20 (MS1), 20 (MS2)
[20:31] RT window set to 0.424464
[20:31] Recommended MS1 mass accuracy setting: 3.4 ppm
[20:31] Main search
[20:34] Removing low confidence identifications
[20:37] Removing interfering precursors
[20:38] Training neural networks on 135884 target and 64837 decoy PSMs
[20:50] Training neural networks on 93527 target and 40599 decoy PSMs
[20:55] Number of IDs at 0.01 FDR: 100397
[20:55] Calculating protein q-values
[20:55] Number of protein isoforms identified at 1% FDR: 9631 (precursor-level), 9839 (protein-level) (inference performed using proteotypic peptides only)
[20:55] Quantification

[20:56] File #6/6
[20:56] Loading run C:\Raw\LFQ_Astral_DDA\LFQ_Astral_DDA_15min_50ng_Condition_B_REP3.raw
[21:05] Pre-processing...
[21:05] 3697 MS1 and 122653 MS2 scans in 1758 (inferred) and 895 (encoded) cycles
[21:05] Calibrating with mass accuracies 20 (MS1), 20 (MS2)
[21:07] RT window set to 0.426205
[21:07] Recommended MS1 mass accuracy setting: 2.9 ppm
[21:07] Main search
[21:10] Removing low confidence identifications
[21:13] Removing interfering precursors
[21:14] Training neural networks on 135933 target and 64748 decoy PSMs
[21:26] Training neural networks on 93394 target and 40494 decoy PSMs
[21:31] Number of IDs at 0.01 FDR: 99526
[21:31] Calculating protein q-values
[21:31] Number of protein isoforms identified at 1% FDR: 9536 (precursor-level), 9841 (protein-level) (inference performed using proteotypic peptides only)
[21:31] Quantification

[21:32] Cross-run analysis
[21:32] Reading quantification information: 6 files
[21:34] Target precursors at 1% global q-value: 116964
[21:34] Quantifying peptides
[21:58] Quantifying proteins
[21:58] Calculating q-values for protein and gene groups
[21:59] Calculating global q-values for protein and gene groups
[21:59] Protein groups with global q-value <= 0.01: 10599
[22:02] Compressed report saved to C:/Out/q-adda.parquet. Use R 'arrow' or Python 'PyArrow' package to process
[22:02] Saving precursor levels matrix
[22:02] Precursor levels matrix (1% precursor and protein group FDR) saved to C:/Out/q-adda.pr_matrix.tsv.
[22:02] Saving protein group levels matrix
[22:02] Protein groups matrix saved to C:/Out/q-adda.pg_matrix.tsv.
[22:02] Saving gene group levels matrix
[22:02] Gene groups matrix saved to C:/Out/q-adda.gg_matrix.tsv.
[22:02] Saving unique genes levels matrix
[22:02] Unique genes matrix saved to C:/Out/q-adda.unique_genes_matrix.tsv.
[22:02] Manifest saved to C:/Out/q-adda.manifest.txt
[22:02] Stats report saved to C:/Out/q-adda.stats.tsv

