
DIA-NN 2.3.2 Academia  (Data-Independent Acquisition by Neural Networks)
Compiled on Jan 22 2026 05:01:57
Current date and time: Thu Jul 23 17:54:17 2026
Logical CPU cores: 128
/opt/diann-2.3.2/diann-linux --lib out-DIANN_libA/WU2.3.2_nomods_report-lib.predicted.speclib --fasta input/ProteoBenchFASTA_Entrapment_Human_with_contaminants_entrapment_pep.fasta --reannotate --f /raw/LFQ_Astral_DIA_15min_50ng_Condition_A_REP1.raw --f /raw/LFQ_Astral_DIA_15min_50ng_Condition_A_REP2.raw --f /raw/LFQ_Astral_DIA_15min_50ng_Condition_A_REP3.raw --f /raw/LFQ_Astral_DIA_15min_50ng_Condition_B_REP1.raw --f /raw/LFQ_Astral_DIA_15min_50ng_Condition_B_REP2.raw --f /raw/LFQ_Astral_DIA_15min_50ng_Condition_B_REP3.raw --threads 24 --qvalue 0.01 --cut  --min-pep-len 7 --max-pep-len 30 --min-pr-charge 2 --max-pr-charge 4 --min-pr-mz 380 --max-pr-mz 980 --min-fr-mz 200 --max-fr-mz 1800 --missed-cleavages 0 --verbose 1 --met-excision --unimod4 --rt-profiling --matrices --pg-level 1 --reanalyse --gen-spec-lib --out-lib out-DIANN_quantB/WU2.3.2_nomods_report-lib.parquet --out out-DIANN_quantB/WU2.3.2_nomods_report.parquet --temp temp-DIANN_quantB 

Library precursors will be reannotated using the FASTA database
Thread number set to 24
Output will be filtered at 0.01 FDR
Min peptide length set to 7
Max peptide length set to 30
Min precursor charge set to 2
Max precursor charge set to 4
Min precursor m/z set to 380
Max precursor m/z set to 980
Min fragment m/z set to 200
Max fragment m/z set to 1800
Maximum number of missed cleavages set to 0
N-terminal methionine excision enabled
Cysteine carbamidomethylation enabled as a fixed modification
The spectral library (if generated) will retain the original spectra but will include empirically-aligned RTs
Precursor/protein x samples expression level matrices will be saved along with the main report
Implicit protein grouping: protein names; this determines which peptides are considered 'proteotypic' and thus affects protein FDR calculation
MBR enabled; .quant files will only be saved to disk during the first pass
A spectral library will be generated
DIA-NN will automatically optimise the mass accuracy for the first run of the experiment, use this mode for preliminary analyses only

6 files will be processed
[0:00] Loading spectral library out-DIANN_libA/WU2.3.2_nomods_report-lib.predicted.speclib
[0:04] Library annotated with sequence database(s): input/ProteoBenchFASTA_Entrapment_Human_with_contaminants_entrapment_pep.fasta
[0:05] Spectral library loaded: 2654746 protein isoforms, 2654663 protein groups and 5042797 precursors in 2707883 elution groups.
[0:05] Loading FASTA input/ProteoBenchFASTA_Entrapment_Human_with_contaminants_entrapment_pep.fasta
[1:08] Reannotating library precursors with information from the FASTA database
[1:32] Finding proteotypic peptides (assuming that the list of UniProt ids provided for each peptide is complete)
[1:32] 5042797 precursors generated
[1:36] Gene names missing for some isoforms
[1:36] Library contains 2654746 proteins, and 0 genes
WARNING: no gene information in the FASTA or library: consider using --ids-to-names
[1:42] Initialising library

First pass: generating a spectral library from DIA data

[1:57] File #1/6
[1:57] Loading run /raw/LFQ_Astral_DIA_15min_50ng_Condition_A_REP1.raw
[2:23] Pre-processing...
[2:24] 2931 MS1 and 293271 MS2 scans in 977 (inferred) and 977 (encoded) cycles, 5038238 precursors in range
[2:24] Calibrating with mass accuracies 21 (MS1), 25 (MS2)
[2:50] RT window set to 1.5748
[2:50] Peak width: 2.808
[2:50] Scan window radius set to 6
[2:50] Recommended MS1 mass accuracy setting: 2.6 ppm
[3:25] Optimised mass accuracy: 7 ppm
[3:36] Main search
[4:44] Removing low confidence identifications
[4:54] Removing interfering precursors
[5:03] Training neural networks on 280705 target and 240206 decoy PSMs
[6:03] IDs at 0.01 FDR: 65205
[6:05] Number of IDs at 0.01 FDR: 65205
[6:05] Calculating protein q-values
[6:06] Number of proteins identified at 1% FDR: 60595 (precursor-level), 60273 (protein-level) (inference performed using proteotypic peptides only)
[6:06] Quantification
[6:07] Quantification information saved to temp-DIANN_quantB/_raw_LFQ_Astral_DIA_15min_50ng_Condition_A_REP1_raw.quant

[6:07] File #2/6
[6:07] Loading run /raw/LFQ_Astral_DIA_15min_50ng_Condition_A_REP2.raw
[6:32] Pre-processing...
[6:33] 2933 MS1 and 293433 MS2 scans in 978 (inferred) and 978 (encoded) cycles, 5038238 precursors in range
[6:33] Calibrating with mass accuracies 21 (MS1), 25 (MS2)
[6:58] RT window set to 1.49137
[6:58] Recommended MS1 mass accuracy setting: 2.8 ppm
[7:05] Main search
[8:08] Removing low confidence identifications
[8:18] Removing interfering precursors
[8:29] Training neural networks on 283291 target and 243333 decoy PSMs
[9:29] IDs at 0.01 FDR: 66497
[9:31] Number of IDs at 0.01 FDR: 66494
[9:31] Calculating protein q-values
[9:32] Number of proteins identified at 1% FDR: 61674 (precursor-level), 61119 (protein-level) (inference performed using proteotypic peptides only)
[9:33] Quantification
[9:34] Quantification information saved to temp-DIANN_quantB/_raw_LFQ_Astral_DIA_15min_50ng_Condition_A_REP2_raw.quant

[9:34] File #3/6
[9:34] Loading run /raw/LFQ_Astral_DIA_15min_50ng_Condition_A_REP3.raw
[9:57] Pre-processing...
[9:59] 2932 MS1 and 293358 MS2 scans in 978 (inferred) and 978 (encoded) cycles, 5038238 precursors in range
[9:59] Calibrating with mass accuracies 21 (MS1), 25 (MS2)
[10:24] RT window set to 1.63091
[10:25] Recommended MS1 mass accuracy setting: 2.6 ppm
[10:33] Main search
[11:42] Removing low confidence identifications
[11:51] Removing interfering precursors
[12:02] Training neural networks on 291351 target and 250881 decoy PSMs
[13:03] IDs at 0.01 FDR: 67390
[13:05] Number of IDs at 0.01 FDR: 67374
[13:05] Calculating protein q-values
[13:06] Number of proteins identified at 1% FDR: 62521 (precursor-level), 62009 (protein-level) (inference performed using proteotypic peptides only)
[13:06] Quantification
[13:08] Quantification information saved to temp-DIANN_quantB/_raw_LFQ_Astral_DIA_15min_50ng_Condition_A_REP3_raw.quant

[13:08] File #4/6
[13:08] Loading run /raw/LFQ_Astral_DIA_15min_50ng_Condition_B_REP1.raw
[13:31] Pre-processing...
[13:32] 2933 MS1 and 293382 MS2 scans in 978 (inferred) and 978 (encoded) cycles, 5038238 precursors in range
[13:33] Calibrating with mass accuracies 21 (MS1), 25 (MS2)
[13:53] RT window set to 1.59763
[13:53] Recommended MS1 mass accuracy setting: 2.9 ppm
[14:01] Main search
[15:10] Removing low confidence identifications
[15:20] Removing interfering precursors
[15:30] Training neural networks on 291799 target and 248212 decoy PSMs
[16:31] IDs at 0.01 FDR: 72068
[16:33] Number of IDs at 0.01 FDR: 72087
[16:33] Calculating protein q-values
[16:34] Number of proteins identified at 1% FDR: 66652 (precursor-level), 66156 (protein-level) (inference performed using proteotypic peptides only)
[16:34] Quantification
[16:36] Quantification information saved to temp-DIANN_quantB/_raw_LFQ_Astral_DIA_15min_50ng_Condition_B_REP1_raw.quant

[16:36] File #5/6
[16:36] Loading run /raw/LFQ_Astral_DIA_15min_50ng_Condition_B_REP2.raw
[17:00] Pre-processing...
[17:02] 2933 MS1 and 293330 MS2 scans in 978 (inferred) and 978 (encoded) cycles, 5038238 precursors in range
[17:02] Calibrating with mass accuracies 21 (MS1), 25 (MS2)
[17:22] RT window set to 1.5444
[17:22] Recommended MS1 mass accuracy setting: 2.8 ppm
[17:29] Main search
[18:35] Removing low confidence identifications
[18:45] Removing interfering precursors
[18:55] Training neural networks on 295077 target and 251560 decoy PSMs
[19:57] IDs at 0.01 FDR: 71349
[19:59] Number of IDs at 0.01 FDR: 71349
[19:59] Calculating protein q-values
[20:00] Number of proteins identified at 1% FDR: 66042 (precursor-level), 65517 (protein-level) (inference performed using proteotypic peptides only)
[20:00] Quantification
[20:02] Quantification information saved to temp-DIANN_quantB/_raw_LFQ_Astral_DIA_15min_50ng_Condition_B_REP2_raw.quant

[20:02] File #6/6
[20:02] Loading run /raw/LFQ_Astral_DIA_15min_50ng_Condition_B_REP3.raw
[20:25] Pre-processing...
[20:26] 2934 MS1 and 293446 MS2 scans in 978 (inferred) and 978 (encoded) cycles, 5038238 precursors in range
[20:27] Calibrating with mass accuracies 21 (MS1), 25 (MS2)
[20:47] RT window set to 1.56217
[20:47] Recommended MS1 mass accuracy setting: 2.6 ppm
[20:54] Main search
[22:02] Removing low confidence identifications
[22:13] Removing interfering precursors
[22:24] Training neural networks on 299333 target and 257200 decoy PSMs
[23:27] IDs at 0.01 FDR: 71421
[23:28] Number of IDs at 0.01 FDR: 71433
[23:28] Calculating protein q-values
[23:29] Number of proteins identified at 1% FDR: 66134 (precursor-level), 65528 (protein-level) (inference performed using proteotypic peptides only)
[23:29] Quantification
[23:31] Quantification information saved to temp-DIANN_quantB/_raw_LFQ_Astral_DIA_15min_50ng_Condition_B_REP3_raw.quant

[23:31] Cross-run analysis
[23:31] Reading quantification information: 6 files
[23:42] Quantifying peptides
[24:06] Assembling protein groups
[24:10] Quantifying proteins
[24:10] Calculating q-values for protein and gene groups
[24:11] Calculating global q-values for protein and gene groups
[24:11] Protein groups with global q-value <= 0.01: 78795
[24:13] Compressed report saved to out-DIANN_quantB/WU2.3.2_nomods_report-first-pass.parquet. Use R 'arrow' or Python 'PyArrow' package to process
[24:13] Saving precursor levels matrix
[24:14] Precursor levels matrix (1% precursor and protein group FDR) saved to out-DIANN_quantB/WU2.3.2_nomods_report-first-pass.pr_matrix.tsv.
[24:14] Manifest saved to out-DIANN_quantB/WU2.3.2_nomods_report-first-pass.manifest.txt
[24:14] Stats report saved to out-DIANN_quantB/WU2.3.2_nomods_report-first-pass.stats.tsv
[24:14] Generating spectral library:
[24:15] 86354 target and 864 decoy precursors saved
[24:16] Spectral library saved to out-DIANN_quantB/WU2.3.2_nomods_report-lib.parquet

[24:22] Loading spectral library out-DIANN_quantB/WU2.3.2_nomods_report-lib.parquet
[24:23] Spectral library loaded: 80372 protein isoforms, 80208 protein groups and 87218 precursors in 80323 elution groups.
[24:23] Loading protein annotations from FASTA input/ProteoBenchFASTA_Entrapment_Human_with_contaminants_entrapment_pep.fasta
[24:45] Annotating library proteins with information from the FASTA database
[24:46] Gene names missing for some isoforms
[24:46] Library contains 80372 proteins, and 0 genes
WARNING: no gene information in the FASTA or library: consider using --ids-to-names
[24:46] Initialising library
[24:47] Saving the library to out-DIANN_quantB/WU2.3.2_nomods_report-lib.parquet.skyline.speclib


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

[24:47] File #1/6
[24:47] Loading run /raw/LFQ_Astral_DIA_15min_50ng_Condition_A_REP1.raw
[25:04] Pre-processing...
[25:05] 2931 MS1 and 293271 MS2 scans in 977 (inferred) and 977 (encoded) cycles, 86354 precursors in range
[25:05] Calibrating with mass accuracies 21 (MS1), 25 (MS2)
[25:05] RT window set to 0.446474
[25:05] Recommended MS1 mass accuracy setting: 3.2 ppm
[25:06] Main search
[25:07] Removing low confidence identifications
[25:09] Removing interfering precursors
[25:10] Training neural networks on 77867 target and 43309 decoy PSMs
[25:23] IDs at 0.01 FDR: 75689
[25:23] Number of IDs at 0.01 FDR: 75689
[25:23] Calculating protein q-values
[25:23] Number of proteins identified at 1% FDR: 69909 (precursor-level), 68122 (protein-level) (inference performed using proteotypic peptides only)
[25:23] Quantification

[25:24] File #2/6
[25:24] Loading run /raw/LFQ_Astral_DIA_15min_50ng_Condition_A_REP2.raw
[25:41] Pre-processing...
[25:41] 2933 MS1 and 293433 MS2 scans in 978 (inferred) and 978 (encoded) cycles, 86354 precursors in range
[25:41] Calibrating with mass accuracies 21 (MS1), 25 (MS2)
[25:42] RT window set to 0.441329
[25:42] Recommended MS1 mass accuracy setting: 3.2 ppm
[25:42] Main search
[25:43] Removing low confidence identifications
[25:46] Removing interfering precursors
[25:46] Training neural networks on 78207 target and 43474 decoy PSMs
[26:00] IDs at 0.01 FDR: 76290
[26:00] Number of IDs at 0.01 FDR: 76290
[26:00] Calculating protein q-values
[26:00] Number of proteins identified at 1% FDR: 70403 (precursor-level), 68520 (protein-level) (inference performed using proteotypic peptides only)
[26:00] Quantification

[26:01] File #3/6
[26:01] Loading run /raw/LFQ_Astral_DIA_15min_50ng_Condition_A_REP3.raw
[26:17] Pre-processing...
[26:18] 2932 MS1 and 293358 MS2 scans in 978 (inferred) and 978 (encoded) cycles, 86354 precursors in range
[26:18] Calibrating with mass accuracies 21 (MS1), 25 (MS2)
[26:18] RT window set to 0.439703
[26:18] Recommended MS1 mass accuracy setting: 3 ppm
[26:18] Main search
[26:20] Removing low confidence identifications
[26:21] Removing interfering precursors
[26:22] Training neural networks on 78123 target and 43607 decoy PSMs
[26:36] IDs at 0.01 FDR: 76661
[26:36] Number of IDs at 0.01 FDR: 76661
[26:36] Calculating protein q-values
[26:36] Number of proteins identified at 1% FDR: 70687 (precursor-level), 68725 (protein-level) (inference performed using proteotypic peptides only)
[26:36] Quantification

[26:37] File #4/6
[26:37] Loading run /raw/LFQ_Astral_DIA_15min_50ng_Condition_B_REP1.raw
[26:53] Pre-processing...
[26:54] 2933 MS1 and 293382 MS2 scans in 978 (inferred) and 978 (encoded) cycles, 86354 precursors in range
[26:54] Calibrating with mass accuracies 22 (MS1), 25 (MS2)
[26:54] RT window set to 0.463078
[26:54] Recommended MS1 mass accuracy setting: 3.4 ppm
[26:55] Main search
[26:56] Removing low confidence identifications
[26:58] Removing interfering precursors
[26:59] Training neural networks on 79089 target and 44168 decoy PSMs
[27:13] IDs at 0.01 FDR: 79083
[27:13] Number of IDs at 0.01 FDR: 79083
[27:13] Calculating protein q-values
[27:13] Number of proteins identified at 1% FDR: 72847 (precursor-level), 70819 (protein-level) (inference performed using proteotypic peptides only)
[27:13] Quantification

[27:14] File #5/6
[27:14] Loading run /raw/LFQ_Astral_DIA_15min_50ng_Condition_B_REP2.raw
[27:30] Pre-processing...
[27:30] 2933 MS1 and 293330 MS2 scans in 978 (inferred) and 978 (encoded) cycles, 86354 precursors in range
[27:30] Calibrating with mass accuracies 21 (MS1), 25 (MS2)
[27:31] RT window set to 0.46415
[27:31] Recommended MS1 mass accuracy setting: 3.3 ppm
[27:31] Main search
[27:32] Removing low confidence identifications
[27:34] Removing interfering precursors
[27:35] Training neural networks on 79076 target and 44110 decoy PSMs
[27:49] IDs at 0.01 FDR: 78923
[27:49] Number of IDs at 0.01 FDR: 78924
[27:49] Calculating protein q-values
[27:49] Number of proteins identified at 1% FDR: 72747 (precursor-level), 70539 (protein-level) (inference performed using proteotypic peptides only)
[27:49] Quantification

[27:51] File #6/6
[27:51] Loading run /raw/LFQ_Astral_DIA_15min_50ng_Condition_B_REP3.raw
[28:08] Pre-processing...
[28:08] 2934 MS1 and 293446 MS2 scans in 978 (inferred) and 978 (encoded) cycles, 86354 precursors in range
[28:08] Calibrating with mass accuracies 21 (MS1), 25 (MS2)
[28:09] RT window set to 0.463588
[28:09] Recommended MS1 mass accuracy setting: 3.2 ppm
[28:09] Main search
[28:10] Removing low confidence identifications
[28:13] Removing interfering precursors
[28:13] Training neural networks on 79070 target and 43634 decoy PSMs
[28:27] IDs at 0.01 FDR: 79028
[28:27] Number of IDs at 0.01 FDR: 79041
[28:27] Calculating protein q-values
[28:27] Number of proteins identified at 1% FDR: 72864 (precursor-level), 70614 (protein-level) (inference performed using proteotypic peptides only)
[28:27] Quantification

[28:28] Cross-run analysis
[28:28] Reading quantification information: 6 files
[28:30] Quantifying peptides
[29:13] Quantification parameters: 0.359359, 0.00164461, 0.00157404, 0.0132173, 0.048343, 0.0140309, 0.291455, 0.109092, 0.148878, 0.0143857, 0.0476037, 0.0368119, 0.363754, 0.051515, 0.0651854, 0.0119979
[29:24] Quantifying proteins
[29:24] Calculating q-values for protein and gene groups
[29:24] Calculating global q-values for protein and gene groups
[29:24] Protein groups with global q-value <= 0.01: 76785
[29:26] Compressed report saved to out-DIANN_quantB/WU2.3.2_nomods_report.parquet. Use R 'arrow' or Python 'PyArrow' package to process
[29:26] Saving precursor levels matrix
[29:27] Precursor levels matrix (1% precursor and protein group FDR) saved to out-DIANN_quantB/WU2.3.2_nomods_report.pr_matrix.tsv.
[29:27] Saving protein group levels matrix
[29:27] Protein groups matrix saved to out-DIANN_quantB/WU2.3.2_nomods_report.pg_matrix.tsv.
[29:27] Saving gene group levels matrix
[29:27] Gene groups matrix saved to out-DIANN_quantB/WU2.3.2_nomods_report.gg_matrix.tsv.
[29:27] Saving unique genes levels matrix
[29:27] Unique genes matrix saved to out-DIANN_quantB/WU2.3.2_nomods_report.unique_genes_matrix.tsv.
[29:27] Manifest saved to out-DIANN_quantB/WU2.3.2_nomods_report.manifest.txt
[29:27] Stats report saved to out-DIANN_quantB/WU2.3.2_nomods_report.stats.tsv

