
DIA-NN 2.2.0 Academia  (Data-Independent Acquisition by Neural Networks)
Compiled on May 29 2025 21:29:29
Current date and time: Tue Oct 21 01:17:08 2025
CPU: AuthenticAMD AMD EPYC 9334 32-Core Processor
SIMD instructions: AVX AVX2 AVX512CD AVX512F FMA SSE4.1 SSE4.2 SSE4a 
Logical CPU cores: 64
diann.exe --f C:\Data\Adi\2025-10-15_BTK_PD_53\P4_1.raw  --f C:\Data\Adi\2025-10-15_BTK_PD_53\P4_2.raw  --f C:\Data\Adi\2025-10-15_BTK_PD_53\P4_3.raw  --f C:\Data\Adi\2025-10-15_BTK_PD_53\P4_4.raw  --lib C:\Data\Adi\2025-10-15_BTK_PD_53\DIANN\Predicted.predicted.speclib --threads 64 --verbose 1 --out C:\Data\Adi\2025-10-15_BTK_PD_53\DIANN\P4\P4.parquet --qvalue 0.01 --matrices --out-lib C:\Data\Adi\2025-10-15_BTK_PD_53\DIANN\P4\P4_library.parquet --gen-spec-lib --xic --fasta C:\Data\Human_proteome_05_2025_strep.fasta --met-excision --cut K*,R* --missed-cleavages 1 --unimod4 --window 10 --reanalyse --rt-profiling 

Thread number set to 64
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
XICs within 10 seconds from the apex will be extracted for each precursor and saved in .parquet format, a folder will be created next to the main report for the XICs storage
N-terminal methionine excision enabled
In silico digest will involve cuts at K*,R*
Maximum number of missed cleavages set to 1
Cysteine carbamidomethylation enabled as a fixed modification
Scan window radius set to 10
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
DIA-NN will automatically optimise the mass accuracy for the first run of the experiment, use this mode for preliminary analyses only

4 files will be processed
[0:00] Loading spectral library C:\Data\Adi\2025-10-15_BTK_PD_53\DIANN\Predicted.predicted.speclib
[0:02] Library annotated with sequence database(s): C:\Data\Human_proteome_05_2025_strep.fasta
[0:02] Spectral library loaded: 20620 protein isoforms, 29339 protein groups and 2552273 precursors in 1327597 elution groups.
[0:02] Loading protein annotations from FASTA C:\Data\Human_proteome_05_2025_strep.fasta
[0:03] Annotating library proteins with information from the FASTA database
[0:03] Gene names missing for some isoforms
[0:03] Library contains 20620 proteins, and 20372 genes
[0:05] Initialising library

First pass: generating a spectral library from DIA data

[0:12] File #1/4
[0:12] Loading run C:\Data\Adi\2025-10-15_BTK_PD_53\P4_1.raw
[0:22] Pre-processing...
[0:23] 4869 MS1 and 155802 MS2 scans in 4869 (inferred) and 4869 (encoded) cycles, 2319451 precursors in range
[0:24] Calibrating with mass accuracies 22 (MS1), 22 (MS2)
[0:59] RT window set to 9.16092
[0:59] Recommended MS1 mass accuracy setting: 8 ppm
[1:37] Optimised mass accuracy: 10 ppm
[1:47] Main search
[2:20] Removing low confidence identifications
[2:22] Removing interfering precursors
[2:24] Training neural networks on 56771 target and 32060 decoy PSMs
[2:37] Number of IDs at 0.01 FDR: 28510
[2:38] Calculating protein q-values
[2:38] Number of genes identified at 1% FDR: 4273 (precursor-level), 3866 (protein-level) (inference performed using proteotypic peptides only)
[2:38] Quantification
[2:39] Quantification information saved to C:\Data\Adi\2025-10-15_BTK_PD_53\P4_1.raw.quant

[2:39] File #2/4
[2:39] Loading run C:\Data\Adi\2025-10-15_BTK_PD_53\P4_2.raw
[2:48] Pre-processing...
[2:49] 4870 MS1 and 155823 MS2 scans in 4870 (inferred) and 4870 (encoded) cycles, 2319451 precursors in range
[2:50] Calibrating with mass accuracies 22 (MS1), 22 (MS2)
[3:25] RT window set to 8.90963
[3:25] Recommended MS1 mass accuracy setting: 8 ppm
[3:34] Main search
[4:09] Removing low confidence identifications
[4:11] Removing interfering precursors
[4:13] Training neural networks on 60231 target and 34474 decoy PSMs
[4:27] Number of IDs at 0.01 FDR: 30717
[4:27] Calculating protein q-values
[4:28] Number of genes identified at 1% FDR: 4596 (precursor-level), 4181 (protein-level) (inference performed using proteotypic peptides only)
[4:28] Quantification
[4:28] Quantification information saved to C:\Data\Adi\2025-10-15_BTK_PD_53\P4_2.raw.quant

[4:29] File #3/4
[4:29] Loading run C:\Data\Adi\2025-10-15_BTK_PD_53\P4_3.raw
[4:39] Pre-processing...
[4:40] 4869 MS1 and 155797 MS2 scans in 4869 (inferred) and 4869 (encoded) cycles, 2319451 precursors in range
[4:40] Calibrating with mass accuracies 22 (MS1), 22 (MS2)
[5:23] RT window set to 10.4777
[5:23] Recommended MS1 mass accuracy setting: 9 ppm
[5:35] Main search
[6:14] Removing low confidence identifications
[6:16] Removing interfering precursors
[6:18] Training neural networks on 49826 target and 28644 decoy PSMs
[6:30] Number of IDs at 0.01 FDR: 23605
[6:30] Calculating protein q-values
[6:30] Number of genes identified at 1% FDR: 3856 (precursor-level), 3514 (protein-level) (inference performed using proteotypic peptides only)
[6:30] Quantification
[6:31] Quantification information saved to C:\Data\Adi\2025-10-15_BTK_PD_53\P4_3.raw.quant

[6:31] File #4/4
[6:31] Loading run C:\Data\Adi\2025-10-15_BTK_PD_53\P4_4.raw
[6:41] Pre-processing...
[6:42] 4869 MS1 and 155790 MS2 scans in 4869 (inferred) and 4869 (encoded) cycles, 2319451 precursors in range
[6:42] Calibrating with mass accuracies 22 (MS1), 22 (MS2)
[7:17] RT window set to 9.11656
[7:17] Recommended MS1 mass accuracy setting: 8 ppm
[7:26] Main search
[7:57] Removing low confidence identifications
[7:59] Removing interfering precursors
[8:01] Training neural networks on 55343 target and 31196 decoy PSMs
[8:14] Number of IDs at 0.01 FDR: 29306
[8:14] Calculating protein q-values
[8:14] Number of genes identified at 1% FDR: 4448 (precursor-level), 4003 (protein-level) (inference performed using proteotypic peptides only)
[8:14] Quantification
[8:15] Quantification information saved to C:\Data\Adi\2025-10-15_BTK_PD_53\P4_4.raw.quant

[8:15] Cross-run analysis
[8:15] Reading quantification information: 4 files
[8:20] Quantifying peptides
[8:48] Assembling protein groups
[8:49] Quantifying proteins
[8:49] Calculating q-values for protein and gene groups
[8:50] Calculating global q-values for protein and gene groups
[8:50] Protein groups with global q-value <= 0.01: 4504
[8:50] Compressed report saved to C:\Data\Adi\2025-10-15_BTK_PD_53\DIANN\P4\P4-first-pass.parquet. Use R 'arrow' or Python 'PyArrow' package to process
[8:50] Saving precursor levels matrix
[8:50] Precursor levels matrix (1% precursor and protein group FDR) saved to C:\Data\Adi\2025-10-15_BTK_PD_53\DIANN\P4\P4-first-pass.pr_matrix.tsv.
[8:50] Manifest saved to C:\Data\Adi\2025-10-15_BTK_PD_53\DIANN\P4\P4-first-pass.manifest.txt
[8:50] Stats report saved to C:\Data\Adi\2025-10-15_BTK_PD_53\DIANN\P4\P4-first-pass.stats.tsv
[8:50] Generating spectral library:
[8:51] 34771 target and 347 decoy precursors saved
[8:51] Spectral library saved to C:\Data\Adi\2025-10-15_BTK_PD_53\DIANN\P4\P4_library.parquet

[8:52] Loading spectral library C:\Data\Adi\2025-10-15_BTK_PD_53\DIANN\P4\P4_library.parquet
[8:52] Spectral library loaded: 5213 protein isoforms, 5106 protein groups and 35118 precursors in 33474 elution groups.
[8:52] Loading protein annotations from FASTA C:\Data\Human_proteome_05_2025_strep.fasta
[8:53] Annotating library proteins with information from the FASTA database
[8:53] Gene names missing for some isoforms
[8:53] Library contains 5213 proteins, and 5213 genes
[8:53] Initialising library
[8:55] Saving the library to C:\Data\Adi\2025-10-15_BTK_PD_53\DIANN\P4\P4_library.parquet.skyline.speclib


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

[8:55] File #1/4
[8:55] Loading run C:\Data\Adi\2025-10-15_BTK_PD_53\P4_1.raw
[9:04] Pre-processing...
[9:05] 4869 MS1 and 155802 MS2 scans in 4869 (inferred) and 4869 (encoded) cycles, 34771 precursors in range
[9:05] Calibrating with mass accuracies 22 (MS1), 21 (MS2)
[9:06] RT window set to 2.78221
[9:06] Recommended MS1 mass accuracy setting: 9 ppm
[9:06] Main search
[9:06] Removing low confidence identifications
[9:07] Removing interfering precursors
[9:07] Training neural networks on 32185 target and 13193 decoy PSMs
[9:14] Number of IDs at 0.01 FDR: 31074
[9:14] Calculating protein q-values
[9:14] Number of genes identified at 1% FDR: 4401 (precursor-level), 3892 (protein-level) (inference performed using proteotypic peptides only)
[9:14] Quantification
[9:15] XICs saved to C:\Data\Adi\2025-10-15_BTK_PD_53\DIANN\P4/P4_xic/P4_1.xic.parquet

[9:16] File #2/4
[9:16] Loading run C:\Data\Adi\2025-10-15_BTK_PD_53\P4_2.raw
[9:25] Pre-processing...
[9:26] 4870 MS1 and 155823 MS2 scans in 4870 (inferred) and 4870 (encoded) cycles, 34771 precursors in range
[9:26] Calibrating with mass accuracies 22 (MS1), 21 (MS2)
[9:27] RT window set to 2.70071
[9:27] Recommended MS1 mass accuracy setting: 9 ppm
[9:27] Main search
[9:27] Removing low confidence identifications
[9:28] Removing interfering precursors
[9:28] Training neural networks on 32530 target and 13511 decoy PSMs
[9:35] Number of IDs at 0.01 FDR: 31901
[9:35] Calculating protein q-values
[9:35] Number of genes identified at 1% FDR: 4541 (precursor-level), 4114 (protein-level) (inference performed using proteotypic peptides only)
[9:35] Quantification
[9:37] XICs saved to C:\Data\Adi\2025-10-15_BTK_PD_53\DIANN\P4/P4_xic/P4_2.xic.parquet

[9:37] File #3/4
[9:37] Loading run C:\Data\Adi\2025-10-15_BTK_PD_53\P4_3.raw
[9:47] Pre-processing...
[9:47] 4869 MS1 and 155797 MS2 scans in 4869 (inferred) and 4869 (encoded) cycles, 34771 precursors in range
[9:47] Calibrating with mass accuracies 22 (MS1), 21 (MS2)
[9:48] RT window set to 2.75121
[9:48] Recommended MS1 mass accuracy setting: 10 ppm
[9:48] Main search
[9:49] Removing low confidence identifications
[9:49] Removing interfering precursors
[9:50] Training neural networks on 30291 target and 12573 decoy PSMs
[9:56] Number of IDs at 0.01 FDR: 27291
[9:56] Calculating protein q-values
[9:56] Number of genes identified at 1% FDR: 4155 (precursor-level), 3690 (protein-level) (inference performed using proteotypic peptides only)
[9:56] Quantification
[9:58] XICs saved to C:\Data\Adi\2025-10-15_BTK_PD_53\DIANN\P4/P4_xic/P4_3.xic.parquet

[9:58] File #4/4
[9:58] Loading run C:\Data\Adi\2025-10-15_BTK_PD_53\P4_4.raw
[10:07] Pre-processing...
[10:08] 4869 MS1 and 155790 MS2 scans in 4869 (inferred) and 4869 (encoded) cycles, 34771 precursors in range
[10:08] Calibrating with mass accuracies 22 (MS1), 21 (MS2)
[10:08] RT window set to 2.77747
[10:08] Recommended MS1 mass accuracy setting: 8 ppm
[10:09] Main search
[10:09] Removing low confidence identifications
[10:10] Removing interfering precursors
[10:10] Training neural networks on 32057 target and 12262 decoy PSMs
[10:17] Number of IDs at 0.01 FDR: 30901
[10:17] Calculating protein q-values
[10:17] Number of genes identified at 1% FDR: 4440 (precursor-level), 3937 (protein-level) (inference performed using proteotypic peptides only)
[10:17] Quantification
[10:18] XICs saved to C:\Data\Adi\2025-10-15_BTK_PD_53\DIANN\P4/P4_xic/P4_4.xic.parquet

[10:19] Cross-run analysis
[10:19] Reading quantification information: 4 files
[10:19] Quantifying peptides
WARNING: QuantUMS requires 6 or more runs for the optimisation of its hyperparameters to perform best.
[11:09] Quantification parameters: 0.316784, 0.00309645, 0.00116075, 0.0113919, 0.0122546, 0.0117305, 0.0821989, 0.0649325, 0.0846736, 0.0121587, 0.015824, 0.0134377, 0.250329, 0.0551798, 0.075266, 0.0118168
[11:22] Quantifying proteins
[11:22] Calculating q-values for protein and gene groups
[11:22] Calculating global q-values for protein and gene groups
[11:22] Protein groups with global q-value <= 0.01: 4252
[11:23] Compressed report saved to C:\Data\Adi\2025-10-15_BTK_PD_53\DIANN\P4\P4.parquet. Use R 'arrow' or Python 'PyArrow' package to process
[11:23] Saving precursor levels matrix
[11:23] Precursor levels matrix (1% precursor and protein group FDR) saved to C:\Data\Adi\2025-10-15_BTK_PD_53\DIANN\P4\P4.pr_matrix.tsv.
[11:23] Saving protein group levels matrix
[11:23] Protein groups matrix saved to C:\Data\Adi\2025-10-15_BTK_PD_53\DIANN\P4\P4.pg_matrix.tsv.
[11:23] Saving gene group levels matrix
[11:23] Gene groups matrix saved to C:\Data\Adi\2025-10-15_BTK_PD_53\DIANN\P4\P4.gg_matrix.tsv.
[11:23] Saving unique genes levels matrix
[11:23] Unique genes matrix saved to C:\Data\Adi\2025-10-15_BTK_PD_53\DIANN\P4\P4.unique_genes_matrix.tsv.
[11:23] Manifest saved to C:\Data\Adi\2025-10-15_BTK_PD_53\DIANN\P4\P4.manifest.txt
[11:23] Stats report saved to C:\Data\Adi\2025-10-15_BTK_PD_53\DIANN\P4\P4.stats.tsv

