DIA-NN 1.9 (Data-Independent Acquisition by Neural Networks)
Compiled on Jun  8 2024 20:00:31
Current date and time: Mon May 26 09:25:31 2025
CPU: GenuineIntel Intel(R) Core(TM) i7-14700
SIMD instructions: AVX AVX2 FMA SSE4.1 SSE4.2 
Logical CPU cores: 28
diann.exe --f D:\timsTOF_HT\PJ1692_250523_Hirakata_IPMS_timsHT_DIA\PJ1692_Hirakata_1_F_Sh1_IPMS_timsHT_DIA_Slot2-16_1_5431.d  --f D:\timsTOF_HT\PJ1692_250523_Hirakata_IPMS_timsHT_DIA\PJ1692_Hirakata_2_F_Sh2_IPMS_timsHT_DIA_Slot2-18_1_5435.d  --f D:\timsTOF_HT\PJ1692_250523_Hirakata_IPMS_timsHT_DIA\PJ1692_Hirakata_3_F_Sh3_IPMS_timsHT_DIA_Slot2-20_1_5439.d  --f D:\timsTOF_HT\PJ1692_250523_Hirakata_IPMS_timsHT_DIA\PJ1692_Hirakata_4_F_L1_IPMS_timsHT_DIA_Slot2-17_1_5433.d  --f D:\timsTOF_HT\PJ1692_250523_Hirakata_IPMS_timsHT_DIA\PJ1692_Hirakata_5_F_L2_IPMS_timsHT_DIA_Slot2-19_1_5437.d  --f D:\timsTOF_HT\PJ1692_250523_Hirakata_IPMS_timsHT_DIA\PJ1692_Hirakata_6_F_L3_IPMS_timsHT_DIA_Slot2-21_1_5441.d  --lib D:\fasta_SpecLib\DAINN19\uniprotkb_proteome_UP000000803_2025_05_25.predicted.speclib --threads 20 --verbose 1 --out D:\timsTOF_HT\PJ1692_250523_Hirakata_IPMS_timsHT_DIA\PJ1692_result\report.tsv --qvalue 0.01 --matrices --out-lib D:\timsTOF_HT\PJ1692_250523_Hirakata_IPMS_timsHT_DIA\PJ1692_result\report-lib.tsv --gen-spec-lib --reannotate --xic --fasta camprotR_240512_cRAP_20190401_full_tags.fasta --cont-quant-exclude cRAP- --fasta D:\fasta_SpecLib\uniprotkb_proteome_UP000000803_2025_05_25.fasta --met-excision --min-pep-len 7 --max-pep-len 30 --min-pr-mz 300 --max-pr-mz 1800 --min-pr-charge 1 --max-pr-charge 4 --cut K*,R* --missed-cleavages 1 --unimod4 --mass-acc 15 --mass-acc-ms1 15 --use-quant --reanalyse --relaxed-prot-inf --rt-profiling --no-norm 

Thread number set to 20
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
Library precursors will be reannotated using the FASTA database
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
Peptides corresponding to protein sequence IDs tagged with cRAP- will be excluded from normalisation as well as quantification of protein groups that do not include proteins bearing the tag
N-terminal methionine excision enabled
Min peptide length set to 7
Max peptide length set to 30
Min precursor m/z set to 300
Max precursor m/z set to 1800
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
Existing .quant files will be used
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
Normalisation disabled
Mass accuracy will be fixed to 1.5e-05 (MS2) and 1.5e-05 (MS1)
WARNING: combining reuse of .quant files with automatic optimisation of mass accuracies or scan window will lead to results that are different from those of the original analysis that produced the .quant files and is therefore not recommended

6 files will be processed
[0:00] Loading spectral library D:\fasta_SpecLib\DAINN19\uniprotkb_proteome_UP000000803_2025_05_25.predicted.speclib
[0:03] Library annotated with sequence database(s): D:\fasta_SpecLib\uniprotkb_proteome_UP000000803_2025_05_25.fasta
[0:03] Spectral library loaded: 21941 protein isoforms, 26477 protein groups and 2895929 precursors in 901974 elution groups.
[0:03] Loading FASTA camprotR_240512_cRAP_20190401_full_tags.fasta
[0:03] Loading FASTA D:\fasta_SpecLib\uniprotkb_proteome_UP000000803_2025_05_25.fasta
[0:15] Reannotating library precursors with information from the FASTA database
[0:18] Finding proteotypic peptides (assuming that the list of UniProt ids provided for each peptide is complete)
[0:18] 2895929 precursors generated
[0:18] Gene names missing for some isoforms
[0:18] Library contains 21955 proteins, and 14339 genes
[0:18] Initialising library

First pass: generating a spectral library from DIA data
[0:21] Cross-run analysis
[0:21] Reading quantification information: 6 files
[0:21] Quantifying peptides
[0:22] Assembling protein groups
[0:22] Quantifying proteins
[0:22] Calculating q-values for protein and gene groups
[0:23] Calculating global q-values for protein and gene groups
[0:23] Protein groups with global q-value <= 0.01: 1205
[0:23] Compressed report saved to D:\timsTOF_HT\PJ1692_250523_Hirakata_IPMS_timsHT_DIA\PJ1692_result\report-first-pass.parquet. Use R 'arrow' or Python 'PyArrow' package to process
[0:23] Writing report
[0:23] Report saved to D:\timsTOF_HT\PJ1692_250523_Hirakata_IPMS_timsHT_DIA\PJ1692_result\report-first-pass.tsv.
[0:23] Saving precursor levels matrix
[0:23] Precursor levels matrix (1% precursor and protein group FDR) saved to D:\timsTOF_HT\PJ1692_250523_Hirakata_IPMS_timsHT_DIA\PJ1692_result\report-first-pass.pr_matrix.tsv.
[0:23] Stats report saved to D:\timsTOF_HT\PJ1692_250523_Hirakata_IPMS_timsHT_DIA\PJ1692_result\report-first-pass.stats.tsv
[0:23] Generating spectral library:
[0:23] Saving spectral library to D:\timsTOF_HT\PJ1692_250523_Hirakata_IPMS_timsHT_DIA\PJ1692_result\report-lib.tsv
[0:23] 4754 target and 46 decoy precursors saved

[0:24] Loading spectral library D:\timsTOF_HT\PJ1692_250523_Hirakata_IPMS_timsHT_DIA\PJ1692_result\report-lib.tsv
[0:24] Spectral library loaded: 1795 protein isoforms, 1405 protein groups and 4800 precursors in 4749 elution groups.
[0:24] Loading protein annotations from FASTA camprotR_240512_cRAP_20190401_full_tags.fasta
[0:24] Loading protein annotations from FASTA D:\fasta_SpecLib\uniprotkb_proteome_UP000000803_2025_05_25.fasta
[0:24] Annotating library proteins with information from the FASTA database
[0:24] Gene names missing for some isoforms
[0:24] Library contains 1795 proteins, and 1440 genes
[0:24] Initialising library
[0:24] Saving the library to D:\timsTOF_HT\PJ1692_250523_Hirakata_IPMS_timsHT_DIA\PJ1692_result\report-lib.tsv.skyline.speclib


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

[0:24] File #1/6
[0:24] Loading run D:\timsTOF_HT\PJ1692_250523_Hirakata_IPMS_timsHT_DIA\PJ1692_Hirakata_1_F_Sh1_IPMS_timsHT_DIA_Slot2-16_1_5431.d
[0:42] 4754 library precursors are potentially detectable
[0:42] Processing...
[0:42] RT window set to 1.21743
[0:42] Ion mobility window set to 0.0133493
[0:42] Peak width: 3.192
[0:42] Scan window radius set to 7
[0:42] Recommended MS1 mass accuracy setting: 16.4019 ppm
[0:43] Removing low confidence identifications
[0:43] Removing interfering precursors
[0:43] Training neural networks: 4586 targets, 1992 decoys
[0:43] Number of IDs at 0.01 FDR: 4304
[0:43] Calculating protein q-values
[0:43] Number of genes identified at 1% FDR: 1212 (precursor-level), 1020 (protein-level) (inference performed using proteotypic peptides only)
[0:43] Quantification
[0:43] XICs saved to D:\timsTOF_HT\PJ1692_250523_Hirakata_IPMS_timsHT_DIA\PJ1692_result/report_xic/PJ1692_Hirakata_1_F_Sh1_IPMS_timsHT_DIA_Slot2-16_1_5431.xic.parquet

[0:43] File #2/6
[0:43] Loading run D:\timsTOF_HT\PJ1692_250523_Hirakata_IPMS_timsHT_DIA\PJ1692_Hirakata_2_F_Sh2_IPMS_timsHT_DIA_Slot2-18_1_5435.d
[1:00] 4754 library precursors are potentially detectable
[1:00] Processing...
[1:01] RT window set to 1.14222
[1:01] Ion mobility window set to 0.0159806
[1:01] Recommended MS1 mass accuracy setting: 15.7512 ppm
[1:01] Removing low confidence identifications
[1:01] Removing interfering precursors
[1:01] Training neural networks: 4503 targets, 1548 decoys
[1:01] Number of IDs at 0.01 FDR: 4238
[1:01] Calculating protein q-values
[1:01] Number of genes identified at 1% FDR: 1194 (precursor-level), 978 (protein-level) (inference performed using proteotypic peptides only)
[1:01] Quantification
[1:01] XICs saved to D:\timsTOF_HT\PJ1692_250523_Hirakata_IPMS_timsHT_DIA\PJ1692_result/report_xic/PJ1692_Hirakata_2_F_Sh2_IPMS_timsHT_DIA_Slot2-18_1_5435.xic.parquet

[1:01] File #3/6
[1:01] Loading run D:\timsTOF_HT\PJ1692_250523_Hirakata_IPMS_timsHT_DIA\PJ1692_Hirakata_3_F_Sh3_IPMS_timsHT_DIA_Slot2-20_1_5439.d
[1:19] 4754 library precursors are potentially detectable
[1:19] Processing...
[1:20] RT window set to 1.14356
[1:20] Ion mobility window set to 0.0174952
[1:20] Recommended MS1 mass accuracy setting: 15.2431 ppm
[1:20] Removing low confidence identifications
[1:21] Removing interfering precursors
[1:21] Training neural networks: 4310 targets, 1517 decoys
[1:21] Number of IDs at 0.01 FDR: 3853
[1:21] Calculating protein q-values
[1:21] Number of genes identified at 1% FDR: 1086 (precursor-level), 867 (protein-level) (inference performed using proteotypic peptides only)
[1:21] Quantification
[1:21] XICs saved to D:\timsTOF_HT\PJ1692_250523_Hirakata_IPMS_timsHT_DIA\PJ1692_result/report_xic/PJ1692_Hirakata_3_F_Sh3_IPMS_timsHT_DIA_Slot2-20_1_5439.xic.parquet

[1:21] File #4/6
[1:21] Loading run D:\timsTOF_HT\PJ1692_250523_Hirakata_IPMS_timsHT_DIA\PJ1692_Hirakata_4_F_L1_IPMS_timsHT_DIA_Slot2-17_1_5433.d
[1:39] 4754 library precursors are potentially detectable
[1:39] Processing...
[1:40] RT window set to 1.12311
[1:40] Ion mobility window set to 0.0197282
[1:40] Recommended MS1 mass accuracy setting: 13.3687 ppm
[1:40] Removing low confidence identifications
[1:40] Removing interfering precursors
[1:40] Training neural networks: 2844 targets, 1271 decoys
[1:41] Number of IDs at 0.01 FDR: 1628
[1:41] Calculating protein q-values
[1:41] Number of genes identified at 1% FDR: 566 (precursor-level), 482 (protein-level) (inference performed using proteotypic peptides only)
[1:41] Quantification
[1:41] XICs saved to D:\timsTOF_HT\PJ1692_250523_Hirakata_IPMS_timsHT_DIA\PJ1692_result/report_xic/PJ1692_Hirakata_4_F_L1_IPMS_timsHT_DIA_Slot2-17_1_5433.xic.parquet

[1:41] File #5/6
[1:41] Loading run D:\timsTOF_HT\PJ1692_250523_Hirakata_IPMS_timsHT_DIA\PJ1692_Hirakata_5_F_L2_IPMS_timsHT_DIA_Slot2-19_1_5437.d
[1:59] 4754 library precursors are potentially detectable
[1:59] Processing...
[2:00] RT window set to 1.17444
[2:00] Ion mobility window set to 0.0178548
[2:00] Recommended MS1 mass accuracy setting: 14.0825 ppm
[2:00] Removing low confidence identifications
[2:00] Removing interfering precursors
[2:00] Training neural networks: 2940 targets, 1489 decoys
[2:01] Number of IDs at 0.01 FDR: 1619
[2:01] Calculating protein q-values
[2:01] Number of genes identified at 1% FDR: 589 (precursor-level), 424 (protein-level) (inference performed using proteotypic peptides only)
[2:01] Quantification
[2:01] XICs saved to D:\timsTOF_HT\PJ1692_250523_Hirakata_IPMS_timsHT_DIA\PJ1692_result/report_xic/PJ1692_Hirakata_5_F_L2_IPMS_timsHT_DIA_Slot2-19_1_5437.xic.parquet

[2:01] File #6/6
[2:01] Loading run D:\timsTOF_HT\PJ1692_250523_Hirakata_IPMS_timsHT_DIA\PJ1692_Hirakata_6_F_L3_IPMS_timsHT_DIA_Slot2-21_1_5441.d
[2:18] 4754 library precursors are potentially detectable
[2:18] Processing...
[2:19] RT window set to 1.15361
[2:19] Ion mobility window set to 0.0192496
[2:19] Recommended MS1 mass accuracy setting: 13.8465 ppm
[2:20] Removing low confidence identifications
[2:20] Removing interfering precursors
[2:20] Training neural networks: 2464 targets, 1215 decoys
[2:20] Number of IDs at 0.01 FDR: 1139
[2:20] Calculating protein q-values
[2:20] Number of genes identified at 1% FDR: 449 (precursor-level), 331 (protein-level) (inference performed using proteotypic peptides only)
[2:20] Quantification
[2:20] XICs saved to D:\timsTOF_HT\PJ1692_250523_Hirakata_IPMS_timsHT_DIA\PJ1692_result/report_xic/PJ1692_Hirakata_6_F_L3_IPMS_timsHT_DIA_Slot2-21_1_5441.xic.parquet

[2:20] Cross-run analysis
[2:20] Reading quantification information: 6 files
[2:20] Quantifying peptides
[2:22] Quantification parameters: 0.364053, 0.00787461, 0.00484717, 0.0120083, 0.654771, 0.39755, 0.490985, 0.0194054, 0.0971854, 0.0139861, 0.0918653, 0.0554132, 0.655543, 0.140708, 0.229275, 0.0113671
[2:22] Quantifying proteins
[2:22] Calculating q-values for protein and gene groups
[2:22] Calculating global q-values for protein and gene groups
[2:22] Protein groups with global q-value <= 0.01: 1059
[2:22] Compressed report saved to D:\timsTOF_HT\PJ1692_250523_Hirakata_IPMS_timsHT_DIA\PJ1692_result\report.parquet. Use R 'arrow' or Python 'PyArrow' package to process
[2:22] Writing report
[2:22] Report saved to D:\timsTOF_HT\PJ1692_250523_Hirakata_IPMS_timsHT_DIA\PJ1692_result\report.tsv.
[2:22] Saving precursor levels matrix
[2:22] Precursor levels matrix (1% precursor and protein group FDR) saved to D:\timsTOF_HT\PJ1692_250523_Hirakata_IPMS_timsHT_DIA\PJ1692_result\report.pr_matrix.tsv.
[2:22] Saving protein group levels matrix
[2:22] Protein group levels matrix (1% precursor FDR and protein group FDR) saved to D:\timsTOF_HT\PJ1692_250523_Hirakata_IPMS_timsHT_DIA\PJ1692_result\report.pg_matrix.tsv.
[2:22] Saving gene group levels matrix
[2:22] Gene groups levels matrix (1% precursor FDR and protein group FDR) saved to D:\timsTOF_HT\PJ1692_250523_Hirakata_IPMS_timsHT_DIA\PJ1692_result\report.gg_matrix.tsv.
[2:22] Saving unique genes levels matrix
[2:22] Unique genes levels matrix (1% precursor FDR and protein group FDR) saved to D:\timsTOF_HT\PJ1692_250523_Hirakata_IPMS_timsHT_DIA\PJ1692_result\report.unique_genes_matrix.tsv.
[2:22] Stats report saved to D:\timsTOF_HT\PJ1692_250523_Hirakata_IPMS_timsHT_DIA\PJ1692_result\report.stats.tsv

