DIA-NN 1.9.2 (Data-Independent Acquisition by Neural Networks)
Compiled on Oct 31 2024 04:27:44
Current date and time: Thu Jan 16 12:39:23 2025
Logical CPU cores: 128
diann-1.9.2/diann-linux --f raw_diapasef_2025/ttSCP_diaPASEF_Condition_A_Sample_Alpha_01_11494.d --f ./raw_diapasef_2025/ttSCP_diaPASEF_Condition_A_Sample_Alpha_02_11500.d --f ./raw_diapasef_2025/ttSCP_diaPASEF_Condition_A_Sample_Alpha_03_11506.d --f ./raw_diapasef_2025/ttSCP_diaPASEF_Condition_B_Sample_Alpha_01_11496.d --f ./raw_diapasef_2025/ttSCP_diaPASEF_Condition_B_Sample_Alpha_02_11502.d --f ./raw_diapasef_2025/ttSCP_diaPASEF_Condition_B_Sample_Alpha_03_11508.d --lib  --threads 100 --verbose 1 --out ./run_output/diann_1.9.2_legacyquant/report.tsv --qvalue 0.01 --matrices --out-lib ./run_output/diann_1.9.2_legacyquant/report-lib.parquet --gen-spec-lib --predictor --fasta ./ProteoBenchFASTA_DDAQuantification.fasta --fasta-search --min-fr-mz 50 --max-fr-mz 2000 --met-excision --min-pep-len 6 --max-pep-len 30 --min-pr-mz 400 --max-pr-mz 1000 --min-pr-charge 1 --max-pr-charge 4 --cut K*,R* --missed-cleavages 1 --unimod4 --var-mods 1 --var-mod UniMod:35,15.994915,M --var-mod UniMod:1,42.010565,*n --peptidoforms --reanalyse --relaxed-prot-inf --rt-profiling --direct-quant 

Thread number set to 100
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
Deep learning will be used to generate a new in silico spectral library from peptides provided
DIA-NN will carry out FASTA digest for in silico lib generation
Min fragment m/z set to 50
Max fragment m/z set to 2000
N-terminal methionine excision enabled
Min peptide length set to 6
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 1
Max precursor charge set to 4
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
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
A spectral library will be created from the DIA runs and used to reanalyse them; .quant files will only be saved to disk during the first step
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
DIA-NN will optimise the mass accuracy automatically using the first run in the experiment. This is useful primarily for quick initial analyses, when it is not yet known which mass accuracy setting works best for a particular acquisition scheme.
WARNING: it is strongly recommended to first generate an in silico-predicted library in a separate pipeline step and then use it to process the raw data, now without activating FASTA digest
The following variable modifications will be scored: UniMod:35 UniMod:1 

6 files will be processed
[0:00] Loading FASTA ./ProteoBenchFASTA_DDAQuantification.fasta
[0:04] Processing FASTA
[0:09] Assembling elution groups
[0:14] 5116692 precursors generated
[0:14] Protein names missing for some isoforms
[0:14] Gene names missing for some isoforms
[0:14] Library contains 31685 proteins, and 0 genes
[0:20] [0:30] [1:59] [2:12] [2:15] [2:18] Saving the library to ./run_output/diann_1.9.2_legacyquant/report-lib.predicted.speclib
[2:27] Initialising library
[2:38] Loading spectral library ./run_output/diann_1.9.2_legacyquant/report-lib.predicted.speclib
[2:41] Library annotated with sequence database(s): ./ProteoBenchFASTA_DDAQuantification.fasta
[2:42] Spectral library loaded: 31837 protein isoforms, 51765 protein groups and 5116692 precursors in 2716663 elution groups.
[2:42] Loading protein annotations from FASTA ./ProteoBenchFASTA_DDAQuantification.fasta
[2:42] Annotating library proteins with information from the FASTA database
[2:42] Protein names missing for some isoforms
[2:42] Gene names missing for some isoforms
[2:42] Library contains 31685 proteins, and 0 genes
[2:46] Initialising library

First pass: generating a spectral library from DIA data

[3:04] File #1/6
[3:04] Loading run raw_diapasef_2025/ttSCP_diaPASEF_Condition_A_Sample_Alpha_01_11494.d
WARNING: for most Slice/DIA-PASEF datasets it is better to manually fix both the MS1 and MS2 mass accuracies to values in the range 10-15 ppm
[3:15] 5116692 library precursors are potentially detectable
[3:15] Calibrating with mass accuracies 20 (MS1), 20 (MS2)
[4:05] RT window set to 2.51371
[4:05] Ion mobility window set to 0.0431608
[4:05] Peak width: 4.052
[4:05] Scan window radius set to 8
[4:05] Recommended MS1 mass accuracy setting: 13.8504 ppm
[4:47] Optimised mass accuracy: 11.5251 ppm
[8:50] Removing low confidence identifications
[10:46] Precursors at 1% peptidoform FDR: 61946
[10:47] Removing interfering precursors
[10:51] Training neural networks on 254155 PSMs
[10:56] Number of IDs at 0.01 FDR: 86978
[10:58] Precursors at 1% peptidoform FDR: 71477
[10:59] Calculating protein q-values
[10:59] Number of genes identified at 1% FDR: 0 (precursor-level), 0 (protein-level) (inference performed using proteotypic peptides only)
[10:59] Quantification
[11:00] Precursors with monitored PTMs at 1% FDR: 977 out of 18249 considered
[11:00] Unmodified precursors with monitored PTM sites at 1% FDR: 13869
[11:00] Precursors with PTMs localised (when required) with > 90% confidence: 961 out of 977
[11:01] Quantification information saved to raw_diapasef_2025/ttSCP_diaPASEF_Condition_A_Sample_Alpha_01_11494.d.quant

[11:01] File #2/6
[11:01] Loading run ./raw_diapasef_2025/ttSCP_diaPASEF_Condition_A_Sample_Alpha_02_11500.d
[11:10] 5116692 library precursors are potentially detectable
[11:10] Calibrating with mass accuracies 20 (MS1), 20 (MS2)
[11:49] RT window set to 2.54363
[11:49] Ion mobility window set to 0.0436509
[11:49] Recommended MS1 mass accuracy setting: 14.3672 ppm
[15:45] Removing low confidence identifications
[17:42] Precursors at 1% peptidoform FDR: 63744
[17:42] Removing interfering precursors
[17:47] Training neural networks on 258447 PSMs
[17:52] Number of IDs at 0.01 FDR: 88884
[17:54] Precursors at 1% peptidoform FDR: 72718
[17:55] Calculating protein q-values
[17:55] Number of genes identified at 1% FDR: 0 (precursor-level), 0 (protein-level) (inference performed using proteotypic peptides only)
[17:55] Quantification
[17:55] Precursors with monitored PTMs at 1% FDR: 760 out of 18296 considered
[17:55] Unmodified precursors with monitored PTM sites at 1% FDR: 14303
[17:55] Precursors with PTMs localised (when required) with > 90% confidence: 743 out of 760
[17:57] Quantification information saved to ./raw_diapasef_2025/ttSCP_diaPASEF_Condition_A_Sample_Alpha_02_11500.d.quant

[17:57] File #3/6
[17:57] Loading run ./raw_diapasef_2025/ttSCP_diaPASEF_Condition_A_Sample_Alpha_03_11506.d
[18:05] 5116692 library precursors are potentially detectable
[18:06] Calibrating with mass accuracies 20 (MS1), 20 (MS2)
[18:45] RT window set to 2.495
[18:45] Ion mobility window set to 0.0426057
[18:45] Recommended MS1 mass accuracy setting: 14.6923 ppm
[22:37] Removing low confidence identifications
[24:31] Precursors at 1% peptidoform FDR: 63841
[24:32] Removing interfering precursors
[24:36] Training neural networks on 259102 PSMs
[24:41] Number of IDs at 0.01 FDR: 90220
[24:43] Precursors at 1% peptidoform FDR: 73321
[24:44] Calculating protein q-values
[24:44] Number of genes identified at 1% FDR: 0 (precursor-level), 0 (protein-level) (inference performed using proteotypic peptides only)
[24:44] Quantification
[24:44] Precursors with monitored PTMs at 1% FDR: 868 out of 18738 considered
[24:44] Unmodified precursors with monitored PTM sites at 1% FDR: 14398
[24:45] Precursors with PTMs localised (when required) with > 90% confidence: 852 out of 868
[24:46] Quantification information saved to ./raw_diapasef_2025/ttSCP_diaPASEF_Condition_A_Sample_Alpha_03_11506.d.quant

[24:46] File #4/6
[24:46] Loading run ./raw_diapasef_2025/ttSCP_diaPASEF_Condition_B_Sample_Alpha_01_11496.d
[24:54] 5116692 library precursors are potentially detectable
[24:55] Calibrating with mass accuracies 20 (MS1), 20 (MS2)
[25:32] RT window set to 2.20157
[25:32] Ion mobility window set to 0.0414363
[25:33] Recommended MS1 mass accuracy setting: 14.3894 ppm
[28:57] Removing low confidence identifications
[30:38] Precursors at 1% peptidoform FDR: 62573
[30:39] Removing interfering precursors
[30:43] Training neural networks on 250177 PSMs
[30:48] Number of IDs at 0.01 FDR: 86500
[30:50] Precursors at 1% peptidoform FDR: 71567
[30:51] Calculating protein q-values
[30:51] Number of genes identified at 1% FDR: 0 (precursor-level), 0 (protein-level) (inference performed using proteotypic peptides only)
[30:51] Quantification
[30:51] Precursors with monitored PTMs at 1% FDR: 663 out of 18077 considered
[30:51] Unmodified precursors with monitored PTM sites at 1% FDR: 14233
[30:52] Precursors with PTMs localised (when required) with > 90% confidence: 648 out of 663
[30:53] Quantification information saved to ./raw_diapasef_2025/ttSCP_diaPASEF_Condition_B_Sample_Alpha_01_11496.d.quant

[30:53] File #5/6
[30:53] Loading run ./raw_diapasef_2025/ttSCP_diaPASEF_Condition_B_Sample_Alpha_02_11502.d
[31:01] 5116692 library precursors are potentially detectable
[31:02] Calibrating with mass accuracies 20 (MS1), 20 (MS2)
[31:40] RT window set to 2.05966
[31:40] Ion mobility window set to 0.040266
[31:40] Recommended MS1 mass accuracy setting: 13.8001 ppm
[34:53] Removing low confidence identifications
[36:28] Precursors at 1% peptidoform FDR: 63284
[36:29] Removing interfering precursors
[36:33] Training neural networks on 254095 PSMs
[36:38] Number of IDs at 0.01 FDR: 86783
[36:41] Precursors at 1% peptidoform FDR: 72060
[36:41] Calculating protein q-values
[36:41] Number of genes identified at 1% FDR: 0 (precursor-level), 0 (protein-level) (inference performed using proteotypic peptides only)
[36:41] Quantification
[36:42] Precursors with monitored PTMs at 1% FDR: 656 out of 18220 considered
[36:42] Unmodified precursors with monitored PTM sites at 1% FDR: 14384
[36:42] Precursors with PTMs localised (when required) with > 90% confidence: 639 out of 656
[36:43] Quantification information saved to ./raw_diapasef_2025/ttSCP_diaPASEF_Condition_B_Sample_Alpha_02_11502.d.quant

[36:43] File #6/6
[36:43] Loading run ./raw_diapasef_2025/ttSCP_diaPASEF_Condition_B_Sample_Alpha_03_11508.d
[36:51] 5116692 library precursors are potentially detectable
[36:52] Calibrating with mass accuracies 20 (MS1), 20 (MS2)
[37:31] RT window set to 1.89942
[37:31] Ion mobility window set to 0.0413312
[37:31] Recommended MS1 mass accuracy setting: 14.3761 ppm
[40:36] Removing low confidence identifications
[42:07] Precursors at 1% peptidoform FDR: 63678
[42:08] Removing interfering precursors
[42:12] Training neural networks on 253559 PSMs
[42:17] Number of IDs at 0.01 FDR: 87939
[42:19] Precursors at 1% peptidoform FDR: 72569
[42:20] Calculating protein q-values
[42:20] Number of genes identified at 1% FDR: 0 (precursor-level), 0 (protein-level) (inference performed using proteotypic peptides only)
[42:20] Quantification
[42:20] Precursors with monitored PTMs at 1% FDR: 681 out of 18030 considered
[42:20] Unmodified precursors with monitored PTM sites at 1% FDR: 14519
[42:20] Precursors with PTMs localised (when required) with > 90% confidence: 663 out of 681
[42:22] Quantification information saved to ./raw_diapasef_2025/ttSCP_diaPASEF_Condition_B_Sample_Alpha_03_11508.d.quant

[42:22] Cross-run analysis
[42:22] Reading quantification information: 6 files
[42:26] Quantifying peptides
[42:35] Assembling protein groups
[42:36] Quantifying proteins
[42:36] Calculating q-values for protein and gene groups
[42:38] Calculating global q-values for protein and gene groups
[42:38] Protein groups with global q-value <= 0.01: 10908
[42:40] Compressed report saved to ./run_output/diann_1.9.2_legacyquant/report-first-pass.parquet. Use R 'arrow' or Python 'PyArrow' package to process
[42:40] Writing report
[42:47] Report saved to ./run_output/diann_1.9.2_legacyquant/report-first-pass.tsv.
[42:47] Saving precursor levels matrix
[42:47] Precursor levels matrix (1% precursor and protein group FDR) saved to ./run_output/diann_1.9.2_legacyquant/report-first-pass.pr_matrix.tsv.
[42:47] Manifest saved to ./run_output/diann_1.9.2_legacyquant/report-first-pass.manifest.txt
[42:47] Stats report saved to ./run_output/diann_1.9.2_legacyquant/report-first-pass.stats.tsv
[42:47] Generating spectral library:
[42:49] 110233 target and 1117 decoy precursors saved
[42:49] Spectral library saved to ./run_output/diann_1.9.2_legacyquant/report-lib.parquet

[42:49] Loading spectral library ./run_output/diann_1.9.2_legacyquant/report-lib.parquet
[42:50] Spectral library loaded: 12843 protein isoforms, 12676 protein groups and 111350 precursors in 103693 elution groups.
[42:50] Loading protein annotations from FASTA ./ProteoBenchFASTA_DDAQuantification.fasta
[42:50] Annotating library proteins with information from the FASTA database
[42:50] Gene names missing for some isoforms
[42:50] Library contains 12828 proteins, and 0 genes
[42:50] Initialising library
[42:51] Saving the library to ./run_output/diann_1.9.2_legacyquant/report-lib.parquet.skyline.speclib


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

[42:51] File #1/6
[42:51] Loading run raw_diapasef_2025/ttSCP_diaPASEF_Condition_A_Sample_Alpha_01_11494.d
[42:59] 110233 library precursors are potentially detectable
[42:59] Calibrating with mass accuracies 20 (MS1), 20 (MS2)
[43:02] RT window set to 0.942846
[43:02] Ion mobility window set to 0.01
[43:02] Recommended MS1 mass accuracy setting: 14.5344 ppm
[43:06] Removing low confidence identifications
[43:08] Precursors at 1% peptidoform FDR: 73478
[43:09] Removing interfering precursors
[43:09] Training neural networks on 151143 PSMs
[43:11] Number of IDs at 0.01 FDR: 99617
[43:14] Precursors at 1% peptidoform FDR: 81502
[43:14] Calculating protein q-values
[43:14] Number of genes identified at 1% FDR: 0 (precursor-level), 0 (protein-level) (inference performed using proteotypic peptides only)
[43:14] Quantification
[43:14] Precursors with monitored PTMs at 1% FDR: 1037 out of 20953 considered
[43:14] Unmodified precursors with monitored PTM sites at 1% FDR: 16237
[43:14] Precursors with PTMs localised (when required) with > 90% confidence: 1023 out of 1037

[43:15] File #2/6
[43:15] Loading run ./raw_diapasef_2025/ttSCP_diaPASEF_Condition_A_Sample_Alpha_02_11500.d
[43:23] 110233 library precursors are potentially detectable
[43:23] Calibrating with mass accuracies 20 (MS1), 20 (MS2)
[43:26] RT window set to 0.943357
[43:26] Ion mobility window set to 0.01
[43:26] Recommended MS1 mass accuracy setting: 14.2693 ppm
[43:29] Removing low confidence identifications
[43:32] Precursors at 1% peptidoform FDR: 77403
[43:33] Removing interfering precursors
[43:33] Training neural networks on 152483 PSMs
[43:36] Number of IDs at 0.01 FDR: 101636
[43:38] Precursors at 1% peptidoform FDR: 82580
[43:38] Calculating protein q-values
[43:38] Number of genes identified at 1% FDR: 0 (precursor-level), 0 (protein-level) (inference performed using proteotypic peptides only)
[43:38] Quantification
[43:38] Precursors with monitored PTMs at 1% FDR: 1018 out of 21143 considered
[43:38] Unmodified precursors with monitored PTM sites at 1% FDR: 16466
[43:38] Precursors with PTMs localised (when required) with > 90% confidence: 1004 out of 1018

[43:39] File #3/6
[43:39] Loading run ./raw_diapasef_2025/ttSCP_diaPASEF_Condition_A_Sample_Alpha_03_11506.d
[43:47] 110233 library precursors are potentially detectable
[43:47] Calibrating with mass accuracies 20 (MS1), 20 (MS2)
[43:50] RT window set to 0.943172
[43:50] Ion mobility window set to 0.01
[43:50] Recommended MS1 mass accuracy setting: 14.1291 ppm
[43:54] Removing low confidence identifications
[43:57] Precursors at 1% peptidoform FDR: 77451
[43:57] Removing interfering precursors
[43:58] Training neural networks on 152390 PSMs
[44:00] Number of IDs at 0.01 FDR: 101393
[44:02] Precursors at 1% peptidoform FDR: 82371
[44:02] Calculating protein q-values
[44:02] Number of genes identified at 1% FDR: 0 (precursor-level), 0 (protein-level) (inference performed using proteotypic peptides only)
[44:02] Quantification
[44:02] Precursors with monitored PTMs at 1% FDR: 1036 out of 21184 considered
[44:02] Unmodified precursors with monitored PTM sites at 1% FDR: 16388
[44:02] Precursors with PTMs localised (when required) with > 90% confidence: 1022 out of 1036

[44:03] File #4/6
[44:03] Loading run ./raw_diapasef_2025/ttSCP_diaPASEF_Condition_B_Sample_Alpha_01_11496.d
[44:11] 110233 library precursors are potentially detectable
[44:11] Calibrating with mass accuracies 20 (MS1), 20 (MS2)
[44:14] RT window set to 0.942342
[44:14] Ion mobility window set to 0.01
[44:14] Recommended MS1 mass accuracy setting: 14.4608 ppm
[44:18] Removing low confidence identifications
[44:21] Precursors at 1% peptidoform FDR: 76661
[44:21] Removing interfering precursors
[44:21] Training neural networks on 152125 PSMs
[44:24] Number of IDs at 0.01 FDR: 101077
[44:26] Precursors at 1% peptidoform FDR: 82576
[44:26] Calculating protein q-values
[44:26] Number of genes identified at 1% FDR: 0 (precursor-level), 0 (protein-level) (inference performed using proteotypic peptides only)
[44:26] Quantification
[44:26] Precursors with monitored PTMs at 1% FDR: 1028 out of 21224 considered
[44:26] Unmodified precursors with monitored PTM sites at 1% FDR: 16489
[44:26] Precursors with PTMs localised (when required) with > 90% confidence: 1015 out of 1028

[44:27] File #5/6
[44:27] Loading run ./raw_diapasef_2025/ttSCP_diaPASEF_Condition_B_Sample_Alpha_02_11502.d
[44:35] 110233 library precursors are potentially detectable
[44:35] Calibrating with mass accuracies 20 (MS1), 20 (MS2)
[44:38] RT window set to 0.941594
[44:38] Ion mobility window set to 0.01
[44:38] Recommended MS1 mass accuracy setting: 14.3188 ppm
[44:42] Removing low confidence identifications
[44:45] Precursors at 1% peptidoform FDR: 77755
[44:45] Removing interfering precursors
[44:45] Training neural networks on 152806 PSMs
[44:48] Number of IDs at 0.01 FDR: 102059
[44:50] Precursors at 1% peptidoform FDR: 83264
[44:50] Calculating protein q-values
[44:50] Number of genes identified at 1% FDR: 0 (precursor-level), 0 (protein-level) (inference performed using proteotypic peptides only)
[44:50] Quantification
[44:50] Precursors with monitored PTMs at 1% FDR: 1028 out of 21450 considered
[44:50] Unmodified precursors with monitored PTM sites at 1% FDR: 16592
[44:50] Precursors with PTMs localised (when required) with > 90% confidence: 1017 out of 1028

[44:51] File #6/6
[44:51] Loading run ./raw_diapasef_2025/ttSCP_diaPASEF_Condition_B_Sample_Alpha_03_11508.d
[44:59] 110233 library precursors are potentially detectable
[44:59] Calibrating with mass accuracies 20 (MS1), 20 (MS2)
[45:02] RT window set to 0.943035
[45:02] Ion mobility window set to 0.01
[45:02] Recommended MS1 mass accuracy setting: 14.6357 ppm
[45:06] Removing low confidence identifications
[45:09] Precursors at 1% peptidoform FDR: 78008
[45:09] Removing interfering precursors
[45:09] Training neural networks on 152772 PSMs
[45:12] Number of IDs at 0.01 FDR: 101967
[45:14] Precursors at 1% peptidoform FDR: 83417
[45:14] Calculating protein q-values
[45:14] Number of genes identified at 1% FDR: 0 (precursor-level), 0 (protein-level) (inference performed using proteotypic peptides only)
[45:14] Quantification
[45:14] Precursors with monitored PTMs at 1% FDR: 1051 out of 21431 considered
[45:14] Unmodified precursors with monitored PTM sites at 1% FDR: 16673
[45:14] Precursors with PTMs localised (when required) with > 90% confidence: 1036 out of 1051

[45:15] Cross-run analysis
[45:15] Reading quantification information: 6 files
[45:16] Quantifying peptides
[45:24] Quantifying proteins
[45:24] Calculating q-values for protein and gene groups
[45:24] Calculating global q-values for protein and gene groups
[45:24] Protein groups with global q-value <= 0.01: 10900
[45:26] Compressed report saved to ./run_output/diann_1.9.2_legacyquant/report.parquet. Use R 'arrow' or Python 'PyArrow' package to process
[45:26] Writing report
[45:35] Report saved to ./run_output/diann_1.9.2_legacyquant/report.tsv.
[45:35] Saving precursor levels matrix
[45:35] Precursor levels matrix (1% precursor and protein group FDR) saved to ./run_output/diann_1.9.2_legacyquant/report.pr_matrix.tsv.
[45:35] Saving protein group levels matrix
[45:35] Protein group levels matrix (1% precursor FDR and protein group FDR) saved to ./run_output/diann_1.9.2_legacyquant/report.pg_matrix.tsv.
[45:35] Saving gene group levels matrix
[45:35] Gene groups levels matrix (1% precursor FDR and protein group FDR) saved to ./run_output/diann_1.9.2_legacyquant/report.gg_matrix.tsv.
[45:35] Saving unique genes levels matrix
[45:35] Unique genes levels matrix (1% precursor FDR and protein group FDR) saved to ./run_output/diann_1.9.2_legacyquant/report.unique_genes_matrix.tsv.
[45:35] Manifest saved to ./run_output/diann_1.9.2_legacyquant/report.manifest.txt
[45:35] Stats report saved to ./run_output/diann_1.9.2_legacyquant/report.stats.tsv

