
DIA-NN 2.5.1 Academia  (Data-Independent Acquisition by Neural Networks)
Compiled on Apr 30 2026 02:32:47
Current date and time: Thu Jul 23 22:49:04 2026
Logical CPU cores: 128
/opt/diann-2.5.1/diann-linux --lib out-DIANN_libA/WU2.5.1_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/WU2.5.1_nomods_report-lib.parquet --out out-DIANN_quantB/WU2.5.1_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/WU2.5.1_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 (targets and decoys).
[0:05] Loading FASTA input/ProteoBenchFASTA_Entrapment_Human_with_contaminants_entrapment_pep.fasta
[1:24] Reannotating library precursors with information from the FASTA database
[1:50] Finding proteotypic peptides (assuming that the list of UniProt ids provided for each peptide is complete)
[1:51] 5042797 precursors generated
[1:54] Gene names missing for some isoforms
[1:54] Library contains 2654746 proteins, and 0 genes
WARNING: no gene information in the FASTA or library: consider using --ids-to-names
[2:01] Initialising library

First pass: generating a spectral library from DIA data

[2:18] File #1/6
[2:18] Loading run /raw/LFQ_Astral_DIA_15min_50ng_Condition_A_REP1.raw
[2:36] Pre-processing...
[2:38] 2931 MS1 and 293271 MS2 scans in 977 (inferred) and 977 (encoded) cycles, 5038238 precursors in range
[2:38] Calibrating with mass accuracies 21 (MS1), 25 (MS2)
[3:03] RT window set to 1.5748
[3:03] Peak width: 2.808
[3:03] Scan window radius set to 6
[3:04] Recommended MS1 mass accuracy setting: 2.6 ppm
[3:38] Optimised mass accuracy: 7 ppm
[3:49] Main search
[4:56] Removing low confidence identifications
[5:05] Removing interfering precursors
[5:14] Training neural networks on 267072 target and 223990 decoy PSMs
[6:07] Number of IDs at 0.01 FDR: 66349
[6:07] Calculating protein q-values
[6:09] Number of proteins identified at 1% FDR: 61626 (precursor-level), 60890 (protein-level) (inference performed using proteotypic peptides only)
[6:09] Quantification
[6:11] Quantification information saved to temp-DIANN_quantB/_raw_LFQ_Astral_DIA_15min_50ng_Condition_A_REP1_raw.quant

[6:12] File #2/6
[6:12] Loading run /raw/LFQ_Astral_DIA_15min_50ng_Condition_A_REP2.raw
[6:27] Pre-processing...
[6:29] 2933 MS1 and 293433 MS2 scans in 978 (inferred) and 978 (encoded) cycles, 5038238 precursors in range
[6:29] Calibrating with mass accuracies 21 (MS1), 25 (MS2)
[6:54] RT window set to 1.49137
[6:54] Recommended MS1 mass accuracy setting: 2.8 ppm
[7:01] Main search
[8:03] Removing low confidence identifications
[8:13] Removing interfering precursors
[8:21] Training neural networks on 261859 target and 218509 decoy PSMs
[9:11] Number of IDs at 0.01 FDR: 67316
[9:11] Calculating protein q-values
[9:13] Number of proteins identified at 1% FDR: 62444 (precursor-level), 61797 (protein-level) (inference performed using proteotypic peptides only)
[9:13] Quantification
[9:15] Quantification information saved to temp-DIANN_quantB/_raw_LFQ_Astral_DIA_15min_50ng_Condition_A_REP2_raw.quant

[9:15] File #3/6
[9:15] Loading run /raw/LFQ_Astral_DIA_15min_50ng_Condition_A_REP3.raw
[9:30] Pre-processing...
[9:31] 2932 MS1 and 293358 MS2 scans in 978 (inferred) and 978 (encoded) cycles, 5038238 precursors in range
[9:31] Calibrating with mass accuracies 21 (MS1), 25 (MS2)
[9:57] RT window set to 1.63091
[9:57] Recommended MS1 mass accuracy setting: 2.6 ppm
[10:05] Main search
[11:13] Removing low confidence identifications
[11:22] Removing interfering precursors
[11:31] Training neural networks on 272819 target and 229177 decoy PSMs
[12:25] Number of IDs at 0.01 FDR: 68013
[12:25] Calculating protein q-values
[12:26] Number of proteins identified at 1% FDR: 63140 (precursor-level), 62579 (protein-level) (inference performed using proteotypic peptides only)
[12:26] Quantification
[12:28] Quantification information saved to temp-DIANN_quantB/_raw_LFQ_Astral_DIA_15min_50ng_Condition_A_REP3_raw.quant

[12:28] File #4/6
[12:28] Loading run /raw/LFQ_Astral_DIA_15min_50ng_Condition_B_REP1.raw
[12:43] Pre-processing...
[12:44] 2933 MS1 and 293382 MS2 scans in 978 (inferred) and 978 (encoded) cycles, 5038238 precursors in range
[12:44] Calibrating with mass accuracies 21 (MS1), 25 (MS2)
[13:04] RT window set to 1.59763
[13:05] Recommended MS1 mass accuracy setting: 2.9 ppm
[13:13] Main search
[14:21] Removing low confidence identifications
[14:30] Removing interfering precursors
[14:40] Training neural networks on 282315 target and 237217 decoy PSMs
[15:34] Number of IDs at 0.01 FDR: 72957
[15:34] Calculating protein q-values
[15:36] Number of proteins identified at 1% FDR: 67533 (precursor-level), 66757 (protein-level) (inference performed using proteotypic peptides only)
[15:36] Quantification
[15:38] Quantification information saved to temp-DIANN_quantB/_raw_LFQ_Astral_DIA_15min_50ng_Condition_B_REP1_raw.quant

[15:38] File #5/6
[15:38] Loading run /raw/LFQ_Astral_DIA_15min_50ng_Condition_B_REP2.raw
[15:53] Pre-processing...
[15:55] 2933 MS1 and 293330 MS2 scans in 978 (inferred) and 978 (encoded) cycles, 5038238 precursors in range
[15:55] Calibrating with mass accuracies 21 (MS1), 25 (MS2)
[16:15] RT window set to 1.5444
[16:15] Recommended MS1 mass accuracy setting: 2.8 ppm
[16:22] Main search
[17:27] Removing low confidence identifications
[17:37] Removing interfering precursors
[17:47] Training neural networks on 282689 target and 237028 decoy PSMs
[18:41] Number of IDs at 0.01 FDR: 72050
[18:41] Calculating protein q-values
[18:42] Number of proteins identified at 1% FDR: 66759 (precursor-level), 66114 (protein-level) (inference performed using proteotypic peptides only)
[18:43] Quantification
[18:45] Quantification information saved to temp-DIANN_quantB/_raw_LFQ_Astral_DIA_15min_50ng_Condition_B_REP2_raw.quant

[18:45] File #6/6
[18:45] Loading run /raw/LFQ_Astral_DIA_15min_50ng_Condition_B_REP3.raw
[19:01] Pre-processing...
[19:02] 2934 MS1 and 293446 MS2 scans in 978 (inferred) and 978 (encoded) cycles, 5038238 precursors in range
[19:03] Calibrating with mass accuracies 21 (MS1), 25 (MS2)
[19:23] RT window set to 1.56217
[19:23] Recommended MS1 mass accuracy setting: 2.6 ppm
[19:30] Main search
[20:37] Removing low confidence identifications
[20:47] Removing interfering precursors
[20:56] Training neural networks on 282178 target and 237251 decoy PSMs
[21:51] Number of IDs at 0.01 FDR: 72570
[21:51] Calculating protein q-values
[21:52] Number of proteins identified at 1% FDR: 67239 (precursor-level), 66449 (protein-level) (inference performed using proteotypic peptides only)
[21:52] Quantification
[21:54] Quantification information saved to temp-DIANN_quantB/_raw_LFQ_Astral_DIA_15min_50ng_Condition_B_REP3_raw.quant

[21:54] Cross-run analysis
[21:54] Reading quantification information: 6 files
[22:08] Target precursors at 1% global q-value: 86998
[22:08] Quantifying peptides
[22:35] Assembling protein groups
[22:40] Quantifying proteins
[22:42] Calculating q-values for protein and gene groups
[22:44] Calculating global q-values for protein and gene groups
[22:44] Protein groups with global q-value <= 0.01: 79048
[22:47] Compressed report saved to out-DIANN_quantB/WU2.5.1_nomods_report-first-pass.parquet. Use R 'arrow' or Python 'PyArrow' package to process
[22:47] Saving precursor levels matrix
[22:47] Precursor levels matrix (1% precursor and protein group FDR) saved to out-DIANN_quantB/WU2.5.1_nomods_report-first-pass.pr_matrix.tsv.
[22:47] Manifest saved to out-DIANN_quantB/WU2.5.1_nomods_report-first-pass.manifest.txt
[22:47] Stats report saved to out-DIANN_quantB/WU2.5.1_nomods_report-first-pass.stats.tsv
[22:47] Generating spectral library:
[22:49] 106501 target and 5974 decoy precursors saved
[22:50] Spectral library saved to out-DIANN_quantB/WU2.5.1_nomods_report-lib.parquet

[22:57] Loading spectral library out-DIANN_quantB/WU2.5.1_nomods_report-lib.parquet
[22:59] Spectral library loaded: 103349 protein isoforms, 103104 protein groups and 112475 precursors in 103910 elution groups (targets and decoys).
[22:59] Loading protein annotations from FASTA input/ProteoBenchFASTA_Entrapment_Human_with_contaminants_entrapment_pep.fasta
[23:23] Annotating library proteins with information from the FASTA database
[23:25] Gene names missing for some isoforms
[23:25] Library contains 103349 proteins, and 0 genes
WARNING: no gene information in the FASTA or library: consider using --ids-to-names
[23:25] Initialising library
[23:26] Saving the library to out-DIANN_quantB/WU2.5.1_nomods_report-lib.parquet.skyline.speclib


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

[23:26] File #1/6
[23:26] Loading run /raw/LFQ_Astral_DIA_15min_50ng_Condition_A_REP1.raw
[23:42] Pre-processing...
[23:43] 2931 MS1 and 293271 MS2 scans in 977 (inferred) and 977 (encoded) cycles, 106501 precursors in range
[23:43] Calibrating with mass accuracies 21 (MS1), 25 (MS2)
[23:43] RT window set to 0.432807
[23:43] Recommended MS1 mass accuracy setting: 3 ppm
[23:44] Main search
[23:45] Removing low confidence identifications
[23:48] Removing interfering precursors
[23:49] Training neural networks on 98845 target and 51079 decoy PSMs
[24:03] Number of IDs at 0.01 FDR: 75986
[24:03] Calculating protein q-values
[24:03] Number of proteins identified at 1% FDR: 69952 (precursor-level), 70385 (protein-level) (inference performed using proteotypic peptides only)
[24:03] Quantification

[24:04] File #2/6
[24:04] Loading run /raw/LFQ_Astral_DIA_15min_50ng_Condition_A_REP2.raw
[24:20] Pre-processing...
[24:21] 2933 MS1 and 293433 MS2 scans in 978 (inferred) and 978 (encoded) cycles, 106501 precursors in range
[24:21] Calibrating with mass accuracies 21 (MS1), 25 (MS2)
[24:21] RT window set to 0.429911
[24:21] Recommended MS1 mass accuracy setting: 2.7 ppm
[24:22] Main search
[24:24] Removing low confidence identifications
[24:26] Removing interfering precursors
[24:27] Training neural networks on 99500 target and 51699 decoy PSMs
[24:41] Number of IDs at 0.01 FDR: 77086
[24:41] Calculating protein q-values
[24:41] Number of proteins identified at 1% FDR: 70964 (precursor-level), 72280 (protein-level) (inference performed using proteotypic peptides only)
[24:41] Quantification

[24:42] File #3/6
[24:42] Loading run /raw/LFQ_Astral_DIA_15min_50ng_Condition_A_REP3.raw
[24:57] Pre-processing...
[24:58] 2932 MS1 and 293358 MS2 scans in 978 (inferred) and 978 (encoded) cycles, 106501 precursors in range
[24:58] Calibrating with mass accuracies 21 (MS1), 25 (MS2)
[24:58] RT window set to 0.433046
[24:58] Recommended MS1 mass accuracy setting: 3 ppm
[24:59] Main search
[25:01] Removing low confidence identifications
[25:03] Removing interfering precursors
[25:04] Training neural networks on 99430 target and 51769 decoy PSMs
[25:18] Number of IDs at 0.01 FDR: 78087
[25:18] Calculating protein q-values
[25:18] Number of proteins identified at 1% FDR: 71840 (precursor-level), 72536 (protein-level) (inference performed using proteotypic peptides only)
[25:18] Quantification

[25:19] File #4/6
[25:19] Loading run /raw/LFQ_Astral_DIA_15min_50ng_Condition_B_REP1.raw
[25:35] Pre-processing...
[25:36] 2933 MS1 and 293382 MS2 scans in 978 (inferred) and 978 (encoded) cycles, 106501 precursors in range
[25:36] Calibrating with mass accuracies 21 (MS1), 25 (MS2)
[25:36] RT window set to 0.449012
[25:36] Recommended MS1 mass accuracy setting: 3.4 ppm
[25:37] Main search
[25:39] Removing low confidence identifications
[25:41] Removing interfering precursors
[25:42] Training neural networks on 100646 target and 52853 decoy PSMs
[25:56] Number of IDs at 0.01 FDR: 80612
[25:56] Calculating protein q-values
[25:56] Number of proteins identified at 1% FDR: 74113 (precursor-level), 74555 (protein-level) (inference performed using proteotypic peptides only)
[25:56] Quantification

[25:58] File #5/6
[25:58] Loading run /raw/LFQ_Astral_DIA_15min_50ng_Condition_B_REP2.raw
[26:14] Pre-processing...
[26:15] 2933 MS1 and 293330 MS2 scans in 978 (inferred) and 978 (encoded) cycles, 106501 precursors in range
[26:15] Calibrating with mass accuracies 21 (MS1), 25 (MS2)
[26:15] RT window set to 0.456733
[26:15] Recommended MS1 mass accuracy setting: 3.1 ppm
[26:15] Main search
[26:17] Removing low confidence identifications
[26:20] Removing interfering precursors
[26:21] Training neural networks on 99766 target and 52139 decoy PSMs
[26:35] Number of IDs at 0.01 FDR: 78526
[26:35] Calculating protein q-values
[26:35] Number of proteins identified at 1% FDR: 72254 (precursor-level), 72581 (protein-level) (inference performed using proteotypic peptides only)
[26:35] Quantification

[26:36] File #6/6
[26:36] Loading run /raw/LFQ_Astral_DIA_15min_50ng_Condition_B_REP3.raw
[26:53] Pre-processing...
[26:53] 2934 MS1 and 293446 MS2 scans in 978 (inferred) and 978 (encoded) cycles, 106501 precursors in range
[26:53] Calibrating with mass accuracies 21 (MS1), 25 (MS2)
[26:54] RT window set to 0.45436
[26:54] Recommended MS1 mass accuracy setting: 2.8 ppm
[26:54] Main search
[26:56] Removing low confidence identifications
[26:58] Removing interfering precursors
[26:59] Training neural networks on 100540 target and 52634 decoy PSMs
[27:14] Number of IDs at 0.01 FDR: 79565
[27:14] Calculating protein q-values
[27:14] Number of proteins identified at 1% FDR: 73190 (precursor-level), 74131 (protein-level) (inference performed using proteotypic peptides only)
[27:14] Quantification

[27:15] Cross-run analysis
[27:15] Reading quantification information: 6 files
[27:17] Target precursors at 1% global q-value: 85697
[27:17] Quantifying peptides
[28:05] Quantification parameters: 0.359255, 0.00165378, 0.0015831, 0.0133419, 0.247263, 0.12585, 0.283177, 0.187738, 0.172112, 0.0144749, 0.0496728, 0.0460506, 0.381411, 0.0509678, 0.0616739, 0.0116798
[28:15] Quantifying proteins
[28:16] Calculating q-values for protein and gene groups
[28:16] Calculating global q-values for protein and gene groups
[28:16] Protein groups with global q-value <= 0.01: 79036
[28:18] Compressed report saved to out-DIANN_quantB/WU2.5.1_nomods_report.parquet. Use R 'arrow' or Python 'PyArrow' package to process
[28:18] Saving precursor levels matrix
[28:19] Precursor levels matrix (1% precursor and protein group FDR) saved to out-DIANN_quantB/WU2.5.1_nomods_report.pr_matrix.tsv.
[28:19] Saving protein group levels matrix
[28:19] Protein groups matrix saved to out-DIANN_quantB/WU2.5.1_nomods_report.pg_matrix.tsv.
[28:19] Saving gene group levels matrix
[28:19] Gene groups matrix saved to out-DIANN_quantB/WU2.5.1_nomods_report.gg_matrix.tsv.
[28:19] Saving unique genes levels matrix
[28:19] Unique genes matrix saved to out-DIANN_quantB/WU2.5.1_nomods_report.unique_genes_matrix.tsv.
[28:19] Manifest saved to out-DIANN_quantB/WU2.5.1_nomods_report.manifest.txt
[28:19] Stats report saved to out-DIANN_quantB/WU2.5.1_nomods_report.stats.tsv

