
DIA-NN 2.1.0 Academia  (Data-Independent Acquisition by Neural Networks)
Compiled on Mar 23 2025 15:49:03
Current date and time: Thu May 22 13:28:16 2025
Logical CPU cores: 64
diann-linux --threads 32 --lib ProteoBenchFASTA_DDAQuantification.predicted.speclib --cfg used_search_params.cfg-- --f /g/pcf/01_users/Frank/proteobench/DIA_Astral/LFQ_Astral_DIA_15min_50ng_Condition_A_REP1.raw --f /g/pcf/01_users/Frank/proteobench/DIA_Astral/LFQ_Astral_DIA_15min_50ng_Condition_A_REP3.raw --f /g/pcf/01_users/Frank/proteobench/DIA_Astral/LFQ_Astral_DIA_15min_50ng_Condition_B_REP1.raw --f /g/pcf/01_users/Frank/proteobench/DIA_Astral/LFQ_Astral_DIA_15min_50ng_Condition_B_REP2.raw --f /g/pcf/01_users/Frank/proteobench/DIA_Astral/LFQ_Astral_DIA_15min_50ng_Condition_B_REP3.raw --f /g/pcf/01_users/Frank/proteobench/DIA_Astral/LFQ_Astral_DIA_15min_50ng_Condition_A_REP2.raw 

Thread number set to 32
Output will be filtered at 0.01 FDR
Precursor/protein x samples expression level matrices will be saved along with the main report
N-terminal methionine excision enabled
Min peptide length set to 6
Max peptide length set to 30
Min precursor m/z set to 380
Max precursor m/z set to 980
Min precursor charge set to 2
Max precursor charge set to 4
Min fragment m/z set to 200
Max fragment m/z set to 1800
In silico digest will involve cuts at K*,R*
But excluding cuts at *P
Maximum number of missed cleavages set to 2
Cysteine carbamidomethylation enabled as a fixed modification
Modification Acetyl with mass delta 200 at A will be considered as fixed
Maximum number of variable modifications set to 3
Modification UniMod:35 with mass delta 15.9949 at M will be considered as variable
Modification UniMod:1 with mass delta 42.0106 at *n will be considered as variable
Peptidoform scoring enabled
MBR enabled; .quant files will only be saved to disk during the first pass
Heuristic protein grouping will be used, to reduce the number of protein groups obtained; this mode is recommended for benchmarking protein ID numbers, GO/pathway and system-scale analyses
The spectral library (if generated) will retain the original spectra but will include empirically-aligned RTs
Legacy (direct) quantification mode
Precursor/protein x samples expression level matrices will be saved along with the main report
WARNING: unrecognised option [--]
DIA-NN will automatically optimise the mass accuracy for the first run of the experiment, use this mode for preliminary analyses only
WARNING: protein inference is enabled but no FASTA provided - is this intended?
The following variable modifications will be localised: UniMod:35 UniMod:1 

6 files will be processed
[0:00] Loading spectral library ProteoBenchFASTA_DDAQuantification.predicted.speclib
[0:03] Library annotated with sequence database(s): ProteoBenchFASTA_DDAQuantification.fasta
[0:03] Protein names missing for some isoforms
[0:03] Gene names missing for some isoforms
[0:03] Library contains 31669 proteins, and 0 genes
[0:04] Spectral library loaded: 31813 protein isoforms, 45877 protein groups and 7768155 precursors in 4179633 elution groups.
[0:10] Initialising library

First pass: generating a spectral library from DIA data

[0:34] File #1/6
[0:34] Loading run /g/pcf/01_users/Frank/proteobench/DIA_Astral/LFQ_Astral_DIA_15min_50ng_Condition_A_REP1.raw
[1:30] Pre-processing...
[1:33] 2931 MS1 and 293271 MS2 scans in 977 (inferred) and 977 (encoded) cycles, 7762093 precursors in range
[1:34] Calibrating with mass accuracies 21 (MS1), 25 (MS2)
[2:08] RT window set to 1.14919
[2:08] Peak width: 2.656
[2:08] Scan window radius set to 5
[2:08] Recommended MS1 mass accuracy setting: 2.3 ppm
[2:48] Optimised mass accuracy: 6 ppm
[3:04] Searching decoys
[3:28] Main search
[4:17] Removing low confidence identifications
[4:29] Removing interfering precursors
[4:39] Training neural networks on 212185 target and 130847 decoy PSMs
[5:18] Training neural networks on 212185 target and 131993 decoy PSMs
[5:50] Number of IDs at 0.01 FDR: 102318
[5:51] Precursors at 1% peptidoform FDR: 99594
[5:52] Calculating protein q-values
[5:52] Number of protein isoforms identified at 1% FDR: 10993 (precursor-level), 10040 (protein-level) (inference performed using proteotypic peptides only)
[5:52] Quantification
[5:53] Precursors with scored PTMs at 1% FDR: 3371 out of 3626 considered
[5:53] Precursors with all scored PTM sites unoccupied at 1% FDR: 96223
[5:53] Precursors with PTMs localised (when required) with > 90% confidence: 2373 out of 3371
[5:55] Quantification information saved to /g/pcf/01_users/Frank/proteobench/DIA_Astral/LFQ_Astral_DIA_15min_50ng_Condition_A_REP1.raw.quant

[5:55] File #2/6
[5:55] Loading run /g/pcf/01_users/Frank/proteobench/DIA_Astral/LFQ_Astral_DIA_15min_50ng_Condition_A_REP3.raw
[6:50] Pre-processing...
[6:53] 2932 MS1 and 293358 MS2 scans in 978 (inferred) and 978 (encoded) cycles, 7762093 precursors in range
[6:54] Calibrating with mass accuracies 21 (MS1), 25 (MS2)
[7:28] RT window set to 1.03549
[7:28] Recommended MS1 mass accuracy setting: 2.3 ppm
[7:52] Searching decoys
[8:14] Main search
[8:59] Removing low confidence identifications
[9:10] Removing interfering precursors
[9:20] Training neural networks on 211053 target and 129776 decoy PSMs
[10:00] Training neural networks on 211053 target and 130425 decoy PSMs
[10:31] Number of IDs at 0.01 FDR: 103547
[10:32] Precursors at 1% peptidoform FDR: 100491
[10:33] Calculating protein q-values
[10:34] Number of protein isoforms identified at 1% FDR: 11060 (precursor-level), 10037 (protein-level) (inference performed using proteotypic peptides only)
[10:34] Quantification
[10:34] Precursors with scored PTMs at 1% FDR: 3472 out of 3747 considered
[10:34] Precursors with all scored PTM sites unoccupied at 1% FDR: 97019
[10:34] Precursors with PTMs localised (when required) with > 90% confidence: 2489 out of 3472
[10:36] Quantification information saved to /g/pcf/01_users/Frank/proteobench/DIA_Astral/LFQ_Astral_DIA_15min_50ng_Condition_A_REP3.raw.quant

[10:36] File #3/6
[10:36] Loading run /g/pcf/01_users/Frank/proteobench/DIA_Astral/LFQ_Astral_DIA_15min_50ng_Condition_B_REP1.raw
[11:31] Pre-processing...
[11:34] 2933 MS1 and 293382 MS2 scans in 978 (inferred) and 978 (encoded) cycles, 7762093 precursors in range
[11:35] Calibrating with mass accuracies 21 (MS1), 25 (MS2)
[12:09] RT window set to 1.15766
[12:10] Recommended MS1 mass accuracy setting: 2.5 ppm
[12:36] Searching decoys
[12:59] Main search
[13:49] Removing low confidence identifications
[14:01] Removing interfering precursors
[14:11] Training neural networks on 212597 target and 132425 decoy PSMs
[14:50] Training neural networks on 212597 target and 132539 decoy PSMs
[15:22] Number of IDs at 0.01 FDR: 103887
[15:23] Precursors at 1% peptidoform FDR: 101253
[15:24] Calculating protein q-values
[15:24] Number of protein isoforms identified at 1% FDR: 10810 (precursor-level), 9708 (protein-level) (inference performed using proteotypic peptides only)
[15:24] Quantification
[15:25] Precursors with scored PTMs at 1% FDR: 4091 out of 4395 considered
[15:25] Precursors with all scored PTM sites unoccupied at 1% FDR: 97162
[15:25] Precursors with PTMs localised (when required) with > 90% confidence: 3044 out of 4091
[15:27] Quantification information saved to /g/pcf/01_users/Frank/proteobench/DIA_Astral/LFQ_Astral_DIA_15min_50ng_Condition_B_REP1.raw.quant

[15:27] File #4/6
[15:27] Loading run /g/pcf/01_users/Frank/proteobench/DIA_Astral/LFQ_Astral_DIA_15min_50ng_Condition_B_REP2.raw
[16:22] Pre-processing...
[16:25] 2933 MS1 and 293330 MS2 scans in 978 (inferred) and 978 (encoded) cycles, 7762093 precursors in range
[16:26] Calibrating with mass accuracies 21 (MS1), 25 (MS2)
[17:01] RT window set to 1.21667
[17:01] Recommended MS1 mass accuracy setting: 2.4 ppm
[17:28] Searching decoys
[17:53] Main search
[18:44] Removing low confidence identifications
[18:56] Removing interfering precursors
[19:06] Training neural networks on 214304 target and 133671 decoy PSMs
[19:47] Training neural networks on 214304 target and 133799 decoy PSMs
[20:19] Number of IDs at 0.01 FDR: 103913
[20:19] Precursors at 1% peptidoform FDR: 101001
[20:20] Calculating protein q-values
[20:21] Number of protein isoforms identified at 1% FDR: 10906 (precursor-level), 9833 (protein-level) (inference performed using proteotypic peptides only)
[20:21] Quantification
[20:22] Precursors with scored PTMs at 1% FDR: 4147 out of 4479 considered
[20:22] Precursors with all scored PTM sites unoccupied at 1% FDR: 96854
[20:22] Precursors with PTMs localised (when required) with > 90% confidence: 3113 out of 4147
[20:23] Quantification information saved to /g/pcf/01_users/Frank/proteobench/DIA_Astral/LFQ_Astral_DIA_15min_50ng_Condition_B_REP2.raw.quant

[20:23] File #5/6
[20:23] Loading run /g/pcf/01_users/Frank/proteobench/DIA_Astral/LFQ_Astral_DIA_15min_50ng_Condition_B_REP3.raw
[21:17] Pre-processing...
[21:20] 2934 MS1 and 293446 MS2 scans in 978 (inferred) and 978 (encoded) cycles, 7762093 precursors in range
[21:21] Calibrating with mass accuracies 21 (MS1), 25 (MS2)
[21:56] RT window set to 1.2472
[21:56] Recommended MS1 mass accuracy setting: 2.4 ppm
[22:18] Searching decoys
[22:44] Main search
[23:35] Removing low confidence identifications
[23:47] Removing interfering precursors
[23:57] Training neural networks on 217885 target and 136757 decoy PSMs
[24:38] Training neural networks on 217885 target and 136191 decoy PSMs
[25:11] Number of IDs at 0.01 FDR: 104768
[25:11] Precursors at 1% peptidoform FDR: 101985
[25:12] Calculating protein q-values
[25:13] Number of protein isoforms identified at 1% FDR: 10915 (precursor-level), 9853 (protein-level) (inference performed using proteotypic peptides only)
[25:13] Quantification
[25:14] Precursors with scored PTMs at 1% FDR: 4163 out of 4413 considered
[25:14] Precursors with all scored PTM sites unoccupied at 1% FDR: 97822
[25:14] Precursors with PTMs localised (when required) with > 90% confidence: 3110 out of 4163
[25:15] Quantification information saved to /g/pcf/01_users/Frank/proteobench/DIA_Astral/LFQ_Astral_DIA_15min_50ng_Condition_B_REP3.raw.quant

[25:15] File #6/6
[25:15] Loading run /g/pcf/01_users/Frank/proteobench/DIA_Astral/LFQ_Astral_DIA_15min_50ng_Condition_A_REP2.raw
[26:06] Pre-processing...
[26:09] 2933 MS1 and 293433 MS2 scans in 978 (inferred) and 978 (encoded) cycles, 7762093 precursors in range
[26:10] Calibrating with mass accuracies 21 (MS1), 25 (MS2)
[26:44] RT window set to 1.11988
[26:44] Recommended MS1 mass accuracy setting: 2.5 ppm
[27:06] Searching decoys
[27:29] Main search
[28:17] Removing low confidence identifications
[28:29] Removing interfering precursors
[28:39] Training neural networks on 213636 target and 131790 decoy PSMs
[29:19] Training neural networks on 213636 target and 132323 decoy PSMs
[29:51] Number of IDs at 0.01 FDR: 103482
[29:51] Precursors at 1% peptidoform FDR: 100688
[29:52] Calculating protein q-values
[29:53] Number of protein isoforms identified at 1% FDR: 11053 (precursor-level), 10049 (protein-level) (inference performed using proteotypic peptides only)
[29:53] Quantification
[29:54] Precursors with scored PTMs at 1% FDR: 3539 out of 3806 considered
[29:54] Precursors with all scored PTM sites unoccupied at 1% FDR: 97149
[29:54] Precursors with PTMs localised (when required) with > 90% confidence: 2531 out of 3539
[29:55] Quantification information saved to /g/pcf/01_users/Frank/proteobench/DIA_Astral/LFQ_Astral_DIA_15min_50ng_Condition_A_REP2.raw.quant

[29:55] Cross-run analysis
[29:55] Reading quantification information: 6 files
[30:18] Quantifying peptides
[32:05] Assembling protein groups
[32:07] Quantifying proteins
[32:08] Calculating q-values for protein and gene groups
[32:09] Calculating global q-values for protein and gene groups
[32:09] Protein groups with global q-value <= 0.01: 11547
[32:12] Compressed report saved to report-first-pass.parquet. Use R 'arrow' or Python 'PyArrow' package to process
[32:12] Saving precursor levels matrix
[32:12] Precursor levels matrix (1% precursor and protein group FDR) saved to report-first-pass.pr_matrix.tsv.
[32:12] Manifest saved to report-first-pass.manifest.txt
[32:12] Stats report saved to report-first-pass.stats.tsv
[32:12] Generating spectral library:
[32:14] 134404 target and 1359 decoy precursors saved
[32:14] Spectral library saved to report-lib.parquet

[32:14] Loading spectral library report-lib.parquet
[32:15] Spectral library loaded: 13387 protein isoforms, 13224 protein groups and 135763 precursors in 126702 elution groups.
[32:16] Initialising library
[32:16] Saving the library to report-lib.parquet.skyline.speclib


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

[32:16] File #1/6
[32:16] Loading run /g/pcf/01_users/Frank/proteobench/DIA_Astral/LFQ_Astral_DIA_15min_50ng_Condition_A_REP1.raw
[33:10] Pre-processing...
[33:12] 2931 MS1 and 293271 MS2 scans in 977 (inferred) and 977 (encoded) cycles, 134404 precursors in range
[33:12] Calibrating with mass accuracies 21 (MS1), 25 (MS2)
[33:13] RT window set to 0.359595
[33:13] Recommended MS1 mass accuracy setting: 2.4 ppm
[33:13] Searching decoys
[33:13] Main search
[33:14] Removing low confidence identifications
[33:17] Removing interfering precursors
[33:18] Training neural networks on 116604 target and 54105 decoy PSMs
[33:33] Training neural networks on 116551 target and 59939 decoy PSMs
[33:48] Number of IDs at 0.01 FDR: 112631
[33:48] Precursors at 1% peptidoform FDR: 110497
[33:48] Calculating protein q-values
[33:48] Number of protein isoforms identified at 1% FDR: 11031 (precursor-level), 10084 (protein-level) (inference performed using proteotypic peptides only)
[33:48] Quantification
[33:49] Precursors with scored PTMs at 1% FDR: 4065 out of 4190 considered
[33:49] Precursors with all scored PTM sites unoccupied at 1% FDR: 106432
[33:49] Precursors with PTMs localised (when required) with > 90% confidence: 2902 out of 4065

[33:49] File #2/6
[33:49] Loading run /g/pcf/01_users/Frank/proteobench/DIA_Astral/LFQ_Astral_DIA_15min_50ng_Condition_A_REP3.raw
[34:45] Pre-processing...
[34:48] 2932 MS1 and 293358 MS2 scans in 978 (inferred) and 978 (encoded) cycles, 134404 precursors in range
[34:48] Calibrating with mass accuracies 21 (MS1), 25 (MS2)
[34:48] RT window set to 0.41083
[34:48] Recommended MS1 mass accuracy setting: 2.6 ppm
[34:49] Searching decoys
[34:49] Main search
[34:50] Removing low confidence identifications
[34:53] Removing interfering precursors
[34:54] Training neural networks on 117229 target and 55836 decoy PSMs
[35:09] Training neural networks on 117181 target and 61522 decoy PSMs
[35:25] Number of IDs at 0.01 FDR: 114144
[35:25] Precursors at 1% peptidoform FDR: 112467
[35:25] Calculating protein q-values
[35:25] Number of protein isoforms identified at 1% FDR: 11065 (precursor-level), 10075 (protein-level) (inference performed using proteotypic peptides only)
[35:25] Quantification
[35:26] Precursors with scored PTMs at 1% FDR: 4162 out of 4280 considered
[35:26] Precursors with all scored PTM sites unoccupied at 1% FDR: 108305
[35:26] Precursors with PTMs localised (when required) with > 90% confidence: 2991 out of 4162

[35:26] File #3/6
[35:26] Loading run /g/pcf/01_users/Frank/proteobench/DIA_Astral/LFQ_Astral_DIA_15min_50ng_Condition_B_REP1.raw
[36:17] Pre-processing...
[36:19] 2933 MS1 and 293382 MS2 scans in 978 (inferred) and 978 (encoded) cycles, 134404 precursors in range
[36:19] Calibrating with mass accuracies 21 (MS1), 25 (MS2)
[36:20] RT window set to 0.4446
[36:20] Recommended MS1 mass accuracy setting: 2.5 ppm
[36:20] Searching decoys
[36:20] Main search
[36:22] Removing low confidence identifications
[36:24] Removing interfering precursors
[36:25] Training neural networks on 117650 target and 57052 decoy PSMs
[36:41] Training neural networks on 117603 target and 62672 decoy PSMs
[36:57] Number of IDs at 0.01 FDR: 114541
[36:57] Precursors at 1% peptidoform FDR: 112940
[36:57] Calculating protein q-values
[36:57] Number of protein isoforms identified at 1% FDR: 10991 (precursor-level), 10052 (protein-level) (inference performed using proteotypic peptides only)
[36:57] Quantification
[36:58] Precursors with scored PTMs at 1% FDR: 4500 out of 4579 considered
[36:58] Precursors with all scored PTM sites unoccupied at 1% FDR: 108440
[36:58] Precursors with PTMs localised (when required) with > 90% confidence: 3328 out of 4500

[36:59] File #4/6
[36:59] Loading run /g/pcf/01_users/Frank/proteobench/DIA_Astral/LFQ_Astral_DIA_15min_50ng_Condition_B_REP2.raw
[37:54] Pre-processing...
[37:57] 2933 MS1 and 293330 MS2 scans in 978 (inferred) and 978 (encoded) cycles, 134404 precursors in range
[37:57] Calibrating with mass accuracies 21 (MS1), 25 (MS2)
[37:57] RT window set to 0.43967
[37:57] Recommended MS1 mass accuracy setting: 2.3 ppm
[37:58] Searching decoys
[37:58] Main search
[37:59] Removing low confidence identifications
[38:02] Removing interfering precursors
[38:03] Training neural networks on 117831 target and 56895 decoy PSMs
[38:18] Training neural networks on 117778 target and 62454 decoy PSMs
[38:34] Number of IDs at 0.01 FDR: 115444
[38:34] Precursors at 1% peptidoform FDR: 113741
[38:34] Calculating protein q-values
[38:34] Number of protein isoforms identified at 1% FDR: 11138 (precursor-level), 10106 (protein-level) (inference performed using proteotypic peptides only)
[38:34] Quantification
[38:35] Precursors with scored PTMs at 1% FDR: 4544 out of 4646 considered
[38:35] Precursors with all scored PTM sites unoccupied at 1% FDR: 109197
[38:35] Precursors with PTMs localised (when required) with > 90% confidence: 3375 out of 4544

[38:35] File #5/6
[38:35] Loading run /g/pcf/01_users/Frank/proteobench/DIA_Astral/LFQ_Astral_DIA_15min_50ng_Condition_B_REP3.raw
[39:31] Pre-processing...
[39:34] 2934 MS1 and 293446 MS2 scans in 978 (inferred) and 978 (encoded) cycles, 134404 precursors in range
[39:34] Calibrating with mass accuracies 21 (MS1), 25 (MS2)
[39:34] RT window set to 0.443132
[39:34] Recommended MS1 mass accuracy setting: 2.5 ppm
[39:34] Searching decoys
[39:35] Main search
[39:36] Removing low confidence identifications
[39:39] Removing interfering precursors
[39:40] Training neural networks on 117634 target and 56791 decoy PSMs
[39:56] Training neural networks on 117584 target and 62672 decoy PSMs
[40:12] Number of IDs at 0.01 FDR: 114919
[40:12] Precursors at 1% peptidoform FDR: 112973
[40:12] Calculating protein q-values
[40:12] Number of protein isoforms identified at 1% FDR: 11104 (precursor-level), 10101 (protein-level) (inference performed using proteotypic peptides only)
[40:12] Quantification
[40:13] Precursors with scored PTMs at 1% FDR: 4523 out of 4608 considered
[40:13] Precursors with all scored PTM sites unoccupied at 1% FDR: 108450
[40:13] Precursors with PTMs localised (when required) with > 90% confidence: 3331 out of 4523

[40:13] File #6/6
[40:13] Loading run /g/pcf/01_users/Frank/proteobench/DIA_Astral/LFQ_Astral_DIA_15min_50ng_Condition_A_REP2.raw
[41:07] Pre-processing...
[41:09] 2933 MS1 and 293433 MS2 scans in 978 (inferred) and 978 (encoded) cycles, 134404 precursors in range
[41:09] Calibrating with mass accuracies 21 (MS1), 25 (MS2)
[41:09] RT window set to 0.357648
[41:09] Recommended MS1 mass accuracy setting: 2.4 ppm
[41:10] Searching decoys
[41:10] Main search
[41:11] Removing low confidence identifications
[41:14] Removing interfering precursors
[41:15] Training neural networks on 116931 target and 53573 decoy PSMs
[41:30] Training neural networks on 116877 target and 59549 decoy PSMs
[41:45] Number of IDs at 0.01 FDR: 113709
[41:45] Precursors at 1% peptidoform FDR: 112197
[41:45] Calculating protein q-values
[41:45] Number of protein isoforms identified at 1% FDR: 11051 (precursor-level), 10076 (protein-level) (inference performed using proteotypic peptides only)
[41:45] Quantification
[41:46] Precursors with scored PTMs at 1% FDR: 4182 out of 4274 considered
[41:46] Precursors with all scored PTM sites unoccupied at 1% FDR: 108015
[41:46] Precursors with PTMs localised (when required) with > 90% confidence: 3014 out of 4182

[41:46] Cross-run analysis
[41:46] Reading quantification information: 6 files
[41:48] Quantifying peptides
[43:29] Quantifying proteins
[43:29] Calculating q-values for protein and gene groups
[43:30] Calculating global q-values for protein and gene groups
[43:30] Protein groups with global q-value <= 0.01: 11058
[43:32] Compressed report saved to report.parquet. Use R 'arrow' or Python 'PyArrow' package to process
[43:32] Saving precursor levels matrix
[43:33] Precursor levels matrix (1% precursor and protein group FDR) saved to report.pr_matrix.tsv.
[43:33] Saving protein group levels matrix
[43:33] Protein groups matrix saved to report.pg_matrix.tsv.
[43:33] Saving gene group levels matrix
[43:33] Gene groups matrix saved to report.gg_matrix.tsv.
[43:33] Saving unique genes levels matrix
[43:33] Unique genes matrix saved to report.unique_genes_matrix.tsv.
[43:33] Manifest saved to report.manifest.txt
[43:33] Stats report saved to report.stats.tsv

