
DIA-NN 2.1.0 Academia  (Data-Independent Acquisition by Neural Networks)
Compiled on Mar 25 2025 10:59:54
Current date and time: Tue Jul  1 17:48:55 2025
CPU: AuthenticAMD AMD Ryzen Threadripper PRO 7995WX 96-Cores
SIMD instructions: AVX AVX2 AVX512CD AVX512F FMA SSE4.1 SSE4.2 SSE4a 
Logical CPU cores: 64
diann.exe --f Z:\mass_data\Exploris480\2025_06\20250626_KI_174_SatoLab_Kumamoto_Univ\20250626_480_DIA_CB_MM_Control_1.raw  --f Z:\mass_data\Exploris480\2025_06\20250626_KI_174_SatoLab_Kumamoto_Univ\20250626_480_DIA_CB_MM_Control_2.raw  --f Z:\mass_data\Exploris480\2025_06\20250626_KI_174_SatoLab_Kumamoto_Univ\20250626_480_DIA_CB_MM_Control_3.raw  --f Z:\mass_data\Exploris480\2025_06\20250626_KI_174_SatoLab_Kumamoto_Univ\20250626_480_DIA_CB_MM_Control_4.raw  --f Z:\mass_data\Exploris480\2025_06\20250626_KI_174_SatoLab_Kumamoto_Univ\20250626_480_DIA_CB_MM_Tmed6_KD_Number2_1.raw  --f Z:\mass_data\Exploris480\2025_06\20250626_KI_174_SatoLab_Kumamoto_Univ\20250626_480_DIA_CB_MM_Tmed6_KD_Number2_2.raw  --f Z:\mass_data\Exploris480\2025_06\20250626_KI_174_SatoLab_Kumamoto_Univ\20250626_480_DIA_CB_MM_Tmed6_KD_Number2_3.raw  --f Z:\mass_data\Exploris480\2025_06\20250626_KI_174_SatoLab_Kumamoto_Univ\20250626_480_DIA_CB_MM_Tmed6_KD_Number2_4.raw  --lib Z:\mass_data\Exploris480\2025_06\20250626_KI_174_SatoLab_Kumamoto_Univ\DIA-NN_20250626_KI_174_SatoLab_Kumamoto_Univ_Control_Tmed6_KD_number2\2025_04_27_Mus_musculus_sp_tr_n54727_cRAP.predicted.speclib --threads 64 --verbose 1 --out Z:\mass_data\Exploris480\2025_06\20250626_KI_174_SatoLab_Kumamoto_Univ\20250626_KI_174_SatoLab_Kumamoto_Univ_Control_Tmed6_KD_number2.parquet --qvalue 0.01 --matrices --out-lib Z:\mass_data\Exploris480\2025_06\20250626_KI_174_SatoLab_Kumamoto_Univ\20250626_KI_174_SatoLab_Kumamoto_Univ_Control_Tmed6_KD_number2.parquet --gen-spec-lib --xic --fasta camprotR_240512_cRAP_20190401_full_tags.fasta --cont-quant-exclude cRAP- --fasta Z:\mass_data\Exploris480\2025_06\20250626_KI_174_SatoLab_Kumamoto_Univ\DIA-NN_20250626_KI_174_SatoLab_Kumamoto_Univ_Control_Tmed6_KD_number2\uniprotkb_proteome_UP000000589_2025_04_27_Mus_musculus_sp_tr_n54727.fasta --met-excision --cut K*,R* --missed-cleavages 1 --unimod4 --reanalyse --rt-profiling --duplicate-proteins 

Thread number set to 64
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
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
In silico digest will involve cuts at K*,R*
Maximum number of missed cleavages set to 1
Cysteine carbamidomethylation enabled as a fixed modification
MBR enabled; .quant files will only be saved to disk during the first pass
The spectral library (if generated) will retain the original spectra but will include empirically-aligned RTs
Duplicate proteins in FASTA files will not be skipped
WARNING: for DIA-NN to switch to the new .raw reader library, please download and install .NET Runtime 8.0.14 or later https://dotnet.microsoft.com/en-us/download/dotnet/8.0
DIA-NN will automatically optimise the mass accuracy for the first run of the experiment, use this mode for preliminary analyses only

8 files will be processed
[0:00] Loading spectral library Z:\mass_data\Exploris480\2025_06\20250626_KI_174_SatoLab_Kumamoto_Univ\DIA-NN_20250626_KI_174_SatoLab_Kumamoto_Univ_Control_Tmed6_KD_number2\2025_04_27_Mus_musculus_sp_tr_n54727_cRAP.predicted.speclib
[0:12] Library annotated with sequence database(s): camprotR_240512_cRAP_20190401_full_tags.fasta; Z:\mass_data\Exploris480\2025_04\20250424_0018-0090_SidoniaLab_Zhang\DIA-NN_0018-0090_SidoniaLab_Zhang\uniprotkb_proteome_UP000000589_2025_04_27_Mus_musculus_sp_tr_n54727.fasta
[0:12] Spectral library loaded: 63038 protein isoforms, 107238 protein groups and 4801707 precursors in 1495650 elution groups.
[0:12] Loading protein annotations from FASTA camprotR_240512_cRAP_20190401_full_tags.fasta
[0:12] Loading protein annotations from FASTA Z:\mass_data\Exploris480\2025_06\20250626_KI_174_SatoLab_Kumamoto_Univ\DIA-NN_20250626_KI_174_SatoLab_Kumamoto_Univ_Control_Tmed6_KD_number2\uniprotkb_proteome_UP000000589_2025_04_27_Mus_musculus_sp_tr_n54727.fasta
[0:13] Annotating library proteins with information from the FASTA database
[0:13] Gene names missing for some isoforms
[0:13] Library contains 54632 proteins, and 22082 genes
[0:15] Initialising library

First pass: generating a spectral library from DIA data

[0:25] File #1/8
[0:25] Loading run Z:\mass_data\Exploris480\2025_06\20250626_KI_174_SatoLab_Kumamoto_Univ\20250626_480_DIA_CB_MM_Control_1.raw
[0:47] Pre-processing...
[0:51] 613 MS1 and 24824 MS2 scans in 613 (inferred) and 307 (encoded) cycles, 1274063 precursors in range
[0:54] Calibrating with mass accuracies 21 (MS1), 22 (MS2)
[1:04] RT window set to 4.65216
[1:04] Peak width: 2.952
[1:04] Scan window radius set to 6
[1:04] Recommended MS1 mass accuracy setting: 15 ppm
[1:20] Optimised mass accuracy: 8 ppm
[1:27] Main search
[1:38] Removing low confidence identifications
[1:43] Removing interfering precursors
[1:48] Training neural networks on 145907 target and 92240 decoy PSMs
[2:07] Number of IDs at 0.01 FDR: 78006
[2:08] Calculating protein q-values
[2:08] Number of genes identified at 1% FDR: 9031 (precursor-level), 8466 (protein-level) (inference performed using proteotypic peptides only)
[2:08] Quantification
[2:14] Quantification information saved to Z:\mass_data\Exploris480\2025_06\20250626_KI_174_SatoLab_Kumamoto_Univ\20250626_480_DIA_CB_MM_Control_1.raw.quant

[2:14] File #2/8
[2:14] Loading run Z:\mass_data\Exploris480\2025_06\20250626_KI_174_SatoLab_Kumamoto_Univ\20250626_480_DIA_CB_MM_Control_2.raw
[2:37] Pre-processing...
[2:41] 613 MS1 and 24819 MS2 scans in 613 (inferred) and 307 (encoded) cycles, 1274063 precursors in range
[2:44] Calibrating with mass accuracies 21 (MS1), 22 (MS2)
[2:54] RT window set to 4.58794
[2:54] Recommended MS1 mass accuracy setting: 13 ppm
[3:06] Main search
[3:17] Removing low confidence identifications
[3:21] Removing interfering precursors
[3:26] Training neural networks on 143968 target and 90247 decoy PSMs
[3:44] Number of IDs at 0.01 FDR: 75589
[3:45] Calculating protein q-values
[3:46] Number of genes identified at 1% FDR: 8972 (precursor-level), 8471 (protein-level) (inference performed using proteotypic peptides only)
[3:46] Quantification
[3:51] Quantification information saved to Z:\mass_data\Exploris480\2025_06\20250626_KI_174_SatoLab_Kumamoto_Univ\20250626_480_DIA_CB_MM_Control_2.raw.quant

[3:51] File #3/8
[3:51] Loading run Z:\mass_data\Exploris480\2025_06\20250626_KI_174_SatoLab_Kumamoto_Univ\20250626_480_DIA_CB_MM_Control_3.raw
[4:13] Pre-processing...
[4:18] 612 MS1 and 24777 MS2 scans in 612 (inferred) and 306 (encoded) cycles, 1274063 precursors in range
[4:20] Calibrating with mass accuracies 21 (MS1), 22 (MS2)
[4:31] RT window set to 4.87399
[4:31] Recommended MS1 mass accuracy setting: 14 ppm
[4:42] Main search
[4:54] Removing low confidence identifications
[4:58] Removing interfering precursors
[5:03] Training neural networks on 140988 target and 87589 decoy PSMs
[5:21] Number of IDs at 0.01 FDR: 75039
[5:22] Calculating protein q-values
[5:22] Number of genes identified at 1% FDR: 9031 (precursor-level), 8425 (protein-level) (inference performed using proteotypic peptides only)
[5:22] Quantification
[5:27] Quantification information saved to Z:\mass_data\Exploris480\2025_06\20250626_KI_174_SatoLab_Kumamoto_Univ\20250626_480_DIA_CB_MM_Control_3.raw.quant

[5:27] File #4/8
[5:27] Loading run Z:\mass_data\Exploris480\2025_06\20250626_KI_174_SatoLab_Kumamoto_Univ\20250626_480_DIA_CB_MM_Control_4.raw
[5:54] Pre-processing...
[5:58] 614 MS1 and 24832 MS2 scans in 614 (inferred) and 307 (encoded) cycles, 1274063 precursors in range
[6:00] Calibrating with mass accuracies 21 (MS1), 22 (MS2)
[6:11] RT window set to 4.35745
[6:11] Recommended MS1 mass accuracy setting: 14 ppm
[6:21] Main search
[6:32] Removing low confidence identifications
[6:37] Removing interfering precursors
[6:41] Training neural networks on 142815 target and 88696 decoy PSMs
[7:00] Number of IDs at 0.01 FDR: 75237
[7:01] Calculating protein q-values
[7:01] Number of genes identified at 1% FDR: 9004 (precursor-level), 8484 (protein-level) (inference performed using proteotypic peptides only)
[7:01] Quantification
[7:06] Quantification information saved to Z:\mass_data\Exploris480\2025_06\20250626_KI_174_SatoLab_Kumamoto_Univ\20250626_480_DIA_CB_MM_Control_4.raw.quant

[7:06] File #5/8
[7:06] Loading run Z:\mass_data\Exploris480\2025_06\20250626_KI_174_SatoLab_Kumamoto_Univ\20250626_480_DIA_CB_MM_Tmed6_KD_Number2_1.raw
[7:39] Pre-processing...
[7:43] 613 MS1 and 24821 MS2 scans in 613 (inferred) and 307 (encoded) cycles, 1274063 precursors in range
[7:46] Calibrating with mass accuracies 21 (MS1), 22 (MS2)
[7:56] RT window set to 4.96471
[7:56] Recommended MS1 mass accuracy setting: 14 ppm
[8:10] Main search
[8:24] Removing low confidence identifications
[8:29] Removing interfering precursors
[8:34] Training neural networks on 143563 target and 92370 decoy PSMs
[8:53] Number of IDs at 0.01 FDR: 75811
[8:54] Calculating protein q-values
[8:54] Number of genes identified at 1% FDR: 9011 (precursor-level), 8535 (protein-level) (inference performed using proteotypic peptides only)
[8:54] Quantification
[9:35] Quantification information saved to Z:\mass_data\Exploris480\2025_06\20250626_KI_174_SatoLab_Kumamoto_Univ\20250626_480_DIA_CB_MM_Tmed6_KD_Number2_1.raw.quant

[9:35] File #6/8
[9:35] Loading run Z:\mass_data\Exploris480\2025_06\20250626_KI_174_SatoLab_Kumamoto_Univ\20250626_480_DIA_CB_MM_Tmed6_KD_Number2_2.raw
[9:59] Pre-processing...
[10:03] 614 MS1 and 24835 MS2 scans in 614 (inferred) and 307 (encoded) cycles, 1274063 precursors in range
[10:06] Calibrating with mass accuracies 21 (MS1), 22 (MS2)
[10:17] RT window set to 5.009
[10:17] Recommended MS1 mass accuracy setting: 13 ppm
[10:30] Main search
[10:42] Removing low confidence identifications
[10:47] Removing interfering precursors
[10:52] Training neural networks on 143269 target and 89868 decoy PSMs
[11:11] Number of IDs at 0.01 FDR: 76255
[11:12] Calculating protein q-values
[11:13] Number of genes identified at 1% FDR: 8978 (precursor-level), 8506 (protein-level) (inference performed using proteotypic peptides only)
[11:13] Quantification
[11:27] Quantification information saved to Z:\mass_data\Exploris480\2025_06\20250626_KI_174_SatoLab_Kumamoto_Univ\20250626_480_DIA_CB_MM_Tmed6_KD_Number2_2.raw.quant

[11:27] File #7/8
[11:27] Loading run Z:\mass_data\Exploris480\2025_06\20250626_KI_174_SatoLab_Kumamoto_Univ\20250626_480_DIA_CB_MM_Tmed6_KD_Number2_3.raw
[12:59] Pre-processing...
[13:03] 613 MS1 and 24824 MS2 scans in 613 (inferred) and 307 (encoded) cycles, 1274063 precursors in range
[13:07] Calibrating with mass accuracies 21 (MS1), 22 (MS2)
[13:17] RT window set to 5.03893
[13:18] Recommended MS1 mass accuracy setting: 14 ppm
[13:30] Main search
[13:42] Removing low confidence identifications
[13:46] Removing interfering precursors
[13:51] Training neural networks on 139953 target and 87276 decoy PSMs
[14:10] Number of IDs at 0.01 FDR: 73913
[14:11] Calculating protein q-values
[14:11] Number of genes identified at 1% FDR: 8937 (precursor-level), 8381 (protein-level) (inference performed using proteotypic peptides only)
[14:11] Quantification
[14:16] Quantification information saved to Z:\mass_data\Exploris480\2025_06\20250626_KI_174_SatoLab_Kumamoto_Univ\20250626_480_DIA_CB_MM_Tmed6_KD_Number2_3.raw.quant

[14:16] File #8/8
[14:16] Loading run Z:\mass_data\Exploris480\2025_06\20250626_KI_174_SatoLab_Kumamoto_Univ\20250626_480_DIA_CB_MM_Tmed6_KD_Number2_4.raw
[14:47] Pre-processing...
[14:51] 614 MS1 and 24827 MS2 scans in 613 (inferred) and 307 (encoded) cycles, 1274063 precursors in range
[14:54] Calibrating with mass accuracies 21 (MS1), 22 (MS2)
[15:04] RT window set to 4.62293
[15:04] Recommended MS1 mass accuracy setting: 16 ppm
[15:18] Main search
[15:30] Removing low confidence identifications
[15:35] Removing interfering precursors
[15:40] Training neural networks on 140925 target and 87046 decoy PSMs
[15:59] Number of IDs at 0.01 FDR: 74460
[16:00] Calculating protein q-values
[16:00] Number of genes identified at 1% FDR: 8927 (precursor-level), 8436 (protein-level) (inference performed using proteotypic peptides only)
[16:00] Quantification
[16:12] Quantification information saved to Z:\mass_data\Exploris480\2025_06\20250626_KI_174_SatoLab_Kumamoto_Univ\20250626_480_DIA_CB_MM_Tmed6_KD_Number2_4.raw.quant

[16:12] Cross-run analysis
[16:12] Reading quantification information: 8 files
[16:23] Quantifying peptides
[17:10] Assembling protein groups
[17:12] Quantifying proteins
[17:12] Calculating q-values for protein and gene groups
[17:14] Calculating global q-values for protein and gene groups
[17:14] Protein groups with global q-value <= 0.01: 9508
[17:16] Compressed report saved to Z:\mass_data\Exploris480\2025_06\20250626_KI_174_SatoLab_Kumamoto_Univ\20250626_KI_174_SatoLab_Kumamoto_Univ_Control_Tmed6_KD_number2-first-pass.parquet. Use R 'arrow' or Python 'PyArrow' package to process
[17:16] Saving precursor levels matrix
[17:18] Precursor levels matrix (1% precursor and protein group FDR) saved to Z:\mass_data\Exploris480\2025_06\20250626_KI_174_SatoLab_Kumamoto_Univ\20250626_KI_174_SatoLab_Kumamoto_Univ_Control_Tmed6_KD_number2-first-pass.pr_matrix.tsv.
[17:18] Manifest saved to Z:\mass_data\Exploris480\2025_06\20250626_KI_174_SatoLab_Kumamoto_Univ\20250626_KI_174_SatoLab_Kumamoto_Univ_Control_Tmed6_KD_number2-first-pass.manifest.txt
[17:18] Stats report saved to Z:\mass_data\Exploris480\2025_06\20250626_KI_174_SatoLab_Kumamoto_Univ\20250626_KI_174_SatoLab_Kumamoto_Univ_Control_Tmed6_KD_number2-first-pass.stats.tsv
[17:18] Generating spectral library:
[17:19] 92148 target and 921 decoy precursors saved
[17:20] Spectral library saved to Z:\mass_data\Exploris480\2025_06\20250626_KI_174_SatoLab_Kumamoto_Univ\20250626_KI_174_SatoLab_Kumamoto_Univ_Control_Tmed6_KD_number2.parquet

[17:20] Loading spectral library Z:\mass_data\Exploris480\2025_06\20250626_KI_174_SatoLab_Kumamoto_Univ\20250626_KI_174_SatoLab_Kumamoto_Univ_Control_Tmed6_KD_number2.parquet
[17:21] Spectral library loaded: 13165 protein isoforms, 10650 protein groups and 93069 precursors in 91206 elution groups.
[17:21] Loading protein annotations from FASTA camprotR_240512_cRAP_20190401_full_tags.fasta
[17:21] Loading protein annotations from FASTA Z:\mass_data\Exploris480\2025_06\20250626_KI_174_SatoLab_Kumamoto_Univ\DIA-NN_20250626_KI_174_SatoLab_Kumamoto_Univ_Control_Tmed6_KD_number2\uniprotkb_proteome_UP000000589_2025_04_27_Mus_musculus_sp_tr_n54727.fasta
[17:21] Annotating library proteins with information from the FASTA database
[17:21] Gene names missing for some isoforms
[17:21] Library contains 10897 proteins, and 10174 genes
[17:22] Initialising library
[17:22] Saving the library to Z:\mass_data\Exploris480\2025_06\20250626_KI_174_SatoLab_Kumamoto_Univ\20250626_KI_174_SatoLab_Kumamoto_Univ_Control_Tmed6_KD_number2.parquet.skyline.speclib


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

[17:25] File #1/8
[17:25] Loading run Z:\mass_data\Exploris480\2025_06\20250626_KI_174_SatoLab_Kumamoto_Univ\20250626_480_DIA_CB_MM_Control_1.raw
[17:48] Pre-processing...
[17:48] 613 MS1 and 24824 MS2 scans in 613 (inferred) and 307 (encoded) cycles, 92148 precursors in range
[17:48] Calibrating with mass accuracies 21 (MS1), 22 (MS2)
[17:48] RT window set to 1.18367
[17:48] Recommended MS1 mass accuracy setting: 16 ppm
[17:49] Main search
[17:50] Removing low confidence identifications
[17:52] Removing interfering precursors
[17:52] Training neural networks on 87570 target and 46555 decoy PSMs
[18:03] Number of IDs at 0.01 FDR: 82087
[18:03] Calculating protein q-values
[18:03] Number of genes identified at 1% FDR: 9051 (precursor-level), 8676 (protein-level) (inference performed using proteotypic peptides only)
[18:03] Quantification
[18:05] XICs saved to Z:\mass_data\Exploris480\2025_06\20250626_KI_174_SatoLab_Kumamoto_Univ/20250626_KI_174_SatoLab_Kumamoto_Univ_Control_Tmed6_KD_number2_xic/20250626_480_DIA_CB_MM_Control_1.xic.parquet

[18:05] File #2/8
[18:05] Loading run Z:\mass_data\Exploris480\2025_06\20250626_KI_174_SatoLab_Kumamoto_Univ\20250626_480_DIA_CB_MM_Control_2.raw
[18:28] Pre-processing...
[18:29] 613 MS1 and 24819 MS2 scans in 613 (inferred) and 307 (encoded) cycles, 92148 precursors in range
[18:29] Calibrating with mass accuracies 21 (MS1), 22 (MS2)
[18:29] RT window set to 1.17477
[18:29] Recommended MS1 mass accuracy setting: 19 ppm
[18:29] Main search
[18:30] Removing low confidence identifications
[18:33] Removing interfering precursors
[18:33] Training neural networks on 87238 target and 46223 decoy PSMs
[18:43] Number of IDs at 0.01 FDR: 80873
[18:44] Calculating protein q-values
[18:44] Number of genes identified at 1% FDR: 9046 (precursor-level), 8697 (protein-level) (inference performed using proteotypic peptides only)
[18:44] Quantification
[18:46] XICs saved to Z:\mass_data\Exploris480\2025_06\20250626_KI_174_SatoLab_Kumamoto_Univ/20250626_KI_174_SatoLab_Kumamoto_Univ_Control_Tmed6_KD_number2_xic/20250626_480_DIA_CB_MM_Control_2.xic.parquet

[18:46] File #3/8
[18:46] Loading run Z:\mass_data\Exploris480\2025_06\20250626_KI_174_SatoLab_Kumamoto_Univ\20250626_480_DIA_CB_MM_Control_3.raw
[19:09] Pre-processing...
[19:09] 612 MS1 and 24777 MS2 scans in 612 (inferred) and 306 (encoded) cycles, 92148 precursors in range
[19:09] Calibrating with mass accuracies 21 (MS1), 22 (MS2)
[19:10] RT window set to 1.16803
[19:10] Recommended MS1 mass accuracy setting: 16 ppm
[19:10] Main search
[19:11] Removing low confidence identifications
[19:13] Removing interfering precursors
[19:13] Training neural networks on 86497 target and 46082 decoy PSMs
[19:24] Number of IDs at 0.01 FDR: 79717
[19:24] Calculating protein q-values
[19:24] Number of genes identified at 1% FDR: 9056 (precursor-level), 8675 (protein-level) (inference performed using proteotypic peptides only)
[19:24] Quantification
[19:26] XICs saved to Z:\mass_data\Exploris480\2025_06\20250626_KI_174_SatoLab_Kumamoto_Univ/20250626_KI_174_SatoLab_Kumamoto_Univ_Control_Tmed6_KD_number2_xic/20250626_480_DIA_CB_MM_Control_3.xic.parquet

[19:26] File #4/8
[19:26] Loading run Z:\mass_data\Exploris480\2025_06\20250626_KI_174_SatoLab_Kumamoto_Univ\20250626_480_DIA_CB_MM_Control_4.raw
[19:49] Pre-processing...
[19:50] 614 MS1 and 24832 MS2 scans in 614 (inferred) and 307 (encoded) cycles, 92148 precursors in range
[19:50] Calibrating with mass accuracies 21 (MS1), 22 (MS2)
[19:50] RT window set to 1.17487
[19:50] Recommended MS1 mass accuracy setting: 17 ppm
[19:51] Main search
[19:52] Removing low confidence identifications
[19:54] Removing interfering precursors
[19:54] Training neural networks on 87094 target and 46335 decoy PSMs
[20:05] Number of IDs at 0.01 FDR: 80584
[20:05] Calculating protein q-values
[20:05] Number of genes identified at 1% FDR: 9061 (precursor-level), 8670 (protein-level) (inference performed using proteotypic peptides only)
[20:05] Quantification
[20:07] XICs saved to Z:\mass_data\Exploris480\2025_06\20250626_KI_174_SatoLab_Kumamoto_Univ/20250626_KI_174_SatoLab_Kumamoto_Univ_Control_Tmed6_KD_number2_xic/20250626_480_DIA_CB_MM_Control_4.xic.parquet

[20:07] File #5/8
[20:07] Loading run Z:\mass_data\Exploris480\2025_06\20250626_KI_174_SatoLab_Kumamoto_Univ\20250626_480_DIA_CB_MM_Tmed6_KD_Number2_1.raw
[20:31] Pre-processing...
[20:31] 613 MS1 and 24821 MS2 scans in 613 (inferred) and 307 (encoded) cycles, 92148 precursors in range
[20:31] Calibrating with mass accuracies 21 (MS1), 23 (MS2)
[20:32] RT window set to 1.17089
[20:32] Recommended MS1 mass accuracy setting: 18 ppm
[20:32] Main search
[20:33] Removing low confidence identifications
[20:35] Removing interfering precursors
[20:36] Training neural networks on 86634 target and 45934 decoy PSMs
[20:46] Number of IDs at 0.01 FDR: 80209
[20:46] Calculating protein q-values
[20:46] Number of genes identified at 1% FDR: 9053 (precursor-level), 8709 (protein-level) (inference performed using proteotypic peptides only)
[20:46] Quantification
[20:49] XICs saved to Z:\mass_data\Exploris480\2025_06\20250626_KI_174_SatoLab_Kumamoto_Univ/20250626_KI_174_SatoLab_Kumamoto_Univ_Control_Tmed6_KD_number2_xic/20250626_480_DIA_CB_MM_Tmed6_KD_Number2_1.xic.parquet

[20:49] File #6/8
[20:49] Loading run Z:\mass_data\Exploris480\2025_06\20250626_KI_174_SatoLab_Kumamoto_Univ\20250626_480_DIA_CB_MM_Tmed6_KD_Number2_2.raw
[21:12] Pre-processing...
[21:13] 614 MS1 and 24835 MS2 scans in 614 (inferred) and 307 (encoded) cycles, 92148 precursors in range
[21:13] Calibrating with mass accuracies 21 (MS1), 22 (MS2)
[21:13] RT window set to 1.18115
[21:13] Recommended MS1 mass accuracy setting: 17 ppm
[21:14] Main search
[21:15] Removing low confidence identifications
[21:17] Removing interfering precursors
[21:18] Training neural networks on 87508 target and 46430 decoy PSMs
[21:28] Number of IDs at 0.01 FDR: 81147
[21:28] Calculating protein q-values
[21:28] Number of genes identified at 1% FDR: 9044 (precursor-level), 8734 (protein-level) (inference performed using proteotypic peptides only)
[21:28] Quantification
[21:30] XICs saved to Z:\mass_data\Exploris480\2025_06\20250626_KI_174_SatoLab_Kumamoto_Univ/20250626_KI_174_SatoLab_Kumamoto_Univ_Control_Tmed6_KD_number2_xic/20250626_480_DIA_CB_MM_Tmed6_KD_Number2_2.xic.parquet

[21:30] File #7/8
[21:30] Loading run Z:\mass_data\Exploris480\2025_06\20250626_KI_174_SatoLab_Kumamoto_Univ\20250626_480_DIA_CB_MM_Tmed6_KD_Number2_3.raw
[21:53] Pre-processing...
[21:54] 613 MS1 and 24824 MS2 scans in 613 (inferred) and 307 (encoded) cycles, 92148 precursors in range
[21:54] Calibrating with mass accuracies 21 (MS1), 22 (MS2)
[21:54] RT window set to 1.1714
[21:54] Recommended MS1 mass accuracy setting: 17 ppm
[21:55] Main search
[21:56] Removing low confidence identifications
[21:58] Removing interfering precursors
[21:58] Training neural networks on 86695 target and 46266 decoy PSMs
[22:08] Number of IDs at 0.01 FDR: 79126
[22:08] Calculating protein q-values
[22:09] Number of genes identified at 1% FDR: 9033 (precursor-level), 8655 (protein-level) (inference performed using proteotypic peptides only)
[22:09] Quantification
[22:11] XICs saved to Z:\mass_data\Exploris480\2025_06\20250626_KI_174_SatoLab_Kumamoto_Univ/20250626_KI_174_SatoLab_Kumamoto_Univ_Control_Tmed6_KD_number2_xic/20250626_480_DIA_CB_MM_Tmed6_KD_Number2_3.xic.parquet

[22:11] File #8/8
[22:11] Loading run Z:\mass_data\Exploris480\2025_06\20250626_KI_174_SatoLab_Kumamoto_Univ\20250626_480_DIA_CB_MM_Tmed6_KD_Number2_4.raw
[22:34] Pre-processing...
[22:35] 614 MS1 and 24827 MS2 scans in 613 (inferred) and 307 (encoded) cycles, 92148 precursors in range
[22:35] Calibrating with mass accuracies 21 (MS1), 22 (MS2)
[22:35] RT window set to 1.16795
[22:35] Recommended MS1 mass accuracy setting: 17 ppm
[22:35] Main search
[22:36] Removing low confidence identifications
[22:38] Removing interfering precursors
[22:39] Training neural networks on 86949 target and 46364 decoy PSMs
[22:49] Number of IDs at 0.01 FDR: 79990
[22:49] Calculating protein q-values
[22:49] Number of genes identified at 1% FDR: 9033 (precursor-level), 8640 (protein-level) (inference performed using proteotypic peptides only)
[22:49] Quantification
[22:52] XICs saved to Z:\mass_data\Exploris480\2025_06\20250626_KI_174_SatoLab_Kumamoto_Univ/20250626_KI_174_SatoLab_Kumamoto_Univ_Control_Tmed6_KD_number2_xic/20250626_480_DIA_CB_MM_Tmed6_KD_Number2_4.xic.parquet

[22:52] Cross-run analysis
[22:52] Reading quantification information: 8 files
[22:52] Quantifying peptides
[25:01] Quantification parameters: 0.285806, 0.00144176, 0.000420842, 0.0119398, 0.0136312, 0.0126648, 0.0783861, 0.0473501, 0.0713759, 0.028457, 0.0497154, 0.0477248, 0.241438, 0.0501571, 0.0529345, 0.0118544
[25:21] Quantifying proteins
[25:22] Calculating q-values for protein and gene groups
[25:22] Calculating global q-values for protein and gene groups
[25:22] Protein groups with global q-value <= 0.01: 9435
[26:02] Compressed report saved to Z:\mass_data\Exploris480\2025_06\20250626_KI_174_SatoLab_Kumamoto_Univ\20250626_KI_174_SatoLab_Kumamoto_Univ_Control_Tmed6_KD_number2.parquet. Use R 'arrow' or Python 'PyArrow' package to process
[26:09] Saving precursor levels matrix
[31:01] Precursor levels matrix (1% precursor and protein group FDR) saved to Z:\mass_data\Exploris480\2025_06\20250626_KI_174_SatoLab_Kumamoto_Univ\20250626_KI_174_SatoLab_Kumamoto_Univ_Control_Tmed6_KD_number2.pr_matrix.tsv.
[31:01] Saving protein group levels matrix
[31:01] Protein groups matrix saved to Z:\mass_data\Exploris480\2025_06\20250626_KI_174_SatoLab_Kumamoto_Univ\20250626_KI_174_SatoLab_Kumamoto_Univ_Control_Tmed6_KD_number2.pg_matrix.tsv.
[31:01] Saving gene group levels matrix
[31:01] Gene groups matrix saved to Z:\mass_data\Exploris480\2025_06\20250626_KI_174_SatoLab_Kumamoto_Univ\20250626_KI_174_SatoLab_Kumamoto_Univ_Control_Tmed6_KD_number2.gg_matrix.tsv.
[31:01] Saving unique genes levels matrix
[31:02] Unique genes matrix saved to Z:\mass_data\Exploris480\2025_06\20250626_KI_174_SatoLab_Kumamoto_Univ\20250626_KI_174_SatoLab_Kumamoto_Univ_Control_Tmed6_KD_number2.unique_genes_matrix.tsv.
[31:03] Manifest saved to Z:\mass_data\Exploris480\2025_06\20250626_KI_174_SatoLab_Kumamoto_Univ\20250626_KI_174_SatoLab_Kumamoto_Univ_Control_Tmed6_KD_number2.manifest.txt
[31:03] Stats report saved to Z:\mass_data\Exploris480\2025_06\20250626_KI_174_SatoLab_Kumamoto_Univ\20250626_KI_174_SatoLab_Kumamoto_Univ_Control_Tmed6_KD_number2.stats.tsv

