DIA-NN 2.0 Academia  (Data-Independent Acquisition by Neural Networks)
Compiled on Jan 28 2025 11:23:41
Current date and time: Wed Apr 30 10:19:17 2025
CPU: GenuineIntel Intel(R) Xeon(R) Gold 6238 CPU @ 2.10GHz
SIMD instructions: AVX AVX2 AVX512CD AVX512F FMA SSE4.1 SSE4.2 
Logical CPU cores: 44
diann.exe --f \\BackupMS4\BackupMS4\MS Raw Files\2025\SCP\20251104_WY_Vero\20250411_S1_WT_1_S1-C1_1_6641.d  --f \\BackupMS4\BackupMS4\MS Raw Files\2025\SCP\20251104_WY_Vero\20250411_S2_WT_2_S1-C2_1_6642.d  --f \\BackupMS4\BackupMS4\MS Raw Files\2025\SCP\20251104_WY_Vero\20250411_S3_WT_3_S1-C3_1_6643.d  --f \\BackupMS4\BackupMS4\MS Raw Files\2025\SCP\20251104_WY_Vero\20250411_S4_A_1_S1-C4_1_6644.d  --f \\BackupMS4\BackupMS4\MS Raw Files\2025\SCP\20251104_WY_Vero\20250411_S5_A_2_S1-C5_1_6645.d  --f \\BackupMS4\BackupMS4\MS Raw Files\2025\SCP\20251104_WY_Vero\20250411_S6_A_3_S1-C6_1_6646.d  --f \\BackupMS4\BackupMS4\MS Raw Files\2025\SCP\20251104_WY_Vero\20250411_S7_B_1_S1-C7_1_6647.d  --f \\BackupMS4\BackupMS4\MS Raw Files\2025\SCP\20251104_WY_Vero\20250411_S8_B_2_S1-C8_1_6648.d  --f \\BackupMS4\BackupMS4\MS Raw Files\2025\SCP\20251104_WY_Vero\20250411_S9_B_3_S1-C9_1_6649.d  --f \\BackupMS4\BackupMS4\MS Raw Files\2025\SCP\20251104_WY_Vero\20250411_S10_Teth_1_S1-B7_1_6638.d  --f \\BackupMS4\BackupMS4\MS Raw Files\2025\SCP\20251104_WY_Vero\20250411_S11_Teth_2_S1-B8_1_6639.d  --f \\BackupMS4\BackupMS4\MS Raw Files\2025\SCP\20251104_WY_Vero\20250411_S12_Teth_3_S1-B9_1_6640.d  --f \\BackupMS4\BackupMS4\MS Raw Files\2025\SCP\20251104_WY_Vero\20250411_S13_AB_1_S1-C10_1_6652.d  --f \\BackupMS4\BackupMS4\MS Raw Files\2025\SCP\20251104_WY_Vero\20250411_S14_AB_2_S1-C11_1_6653.d  --f \\BackupMS4\BackupMS4\MS Raw Files\2025\SCP\20251104_WY_Vero\20250411_S15_AB_3_S1-C12_1_6654.d  --lib  --threads 44 --verbose 1 --out D:\Users_FPL\2025_DIANN\20250430_Green_Monkey\report.parquet --qvalue 0.01 --matrices --out-lib D:\Users_FPL\2025_DIANN\20250430_Green_Monkey\report-lib.parquet --gen-spec-lib --predictor --reannotate --fasta camprotR_240512_cRAP_20190401_full_tags.fasta --cont-quant-exclude cRAP- --fasta D:\Users_FPL\2025_DIANN\20250430_Green_Monkey\UP000029965_60711_Green_Monkey.fasta --fasta-search --min-fr-mz 200 --max-fr-mz 1800 --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 --reanalyse --rt-profiling -tims-scan 

Thread number set to 44
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
Library precursors will be reannotated using the FASTA database
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
DIA-NN will carry out FASTA digest for in silico lib generation
Min fragment m/z set to 200
Max fragment m/z set to 1800
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
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
DIA-NN will automatically optimise the mass accuracy for the first run of the experiment, use this mode for preliminary analyses only
WARNING: incorrect settings, the in silico-predicted library must be generated in a separate pipeline step and then used to process the raw data, now without activating FASTA digest
WARNING: reannotation of the library should not be combined with the analysis of raw data but rather needs to be performed in a separate step

15 files will be processed
[0:00] Loading FASTA camprotR_240512_cRAP_20190401_full_tags.fasta
[0:00] Loading FASTA D:\Users_FPL\2025_DIANN\20250430_Green_Monkey\UP000029965_60711_Green_Monkey.fasta
[0:03] Processing FASTA
[0:06] Assembling elution groups
[0:12] 4057560 precursors generated
[0:12] Gene names missing for some isoforms
[0:12] Library contains 19250 proteins, and 16585 genes
[0:16] [0:25] [6:10] [6:38] [6:41] [6:42] Saving the library to D:\Users_FPL\2025_DIANN\20250430_Green_Monkey\report-lib.predicted.speclib
[6:51] Initialising library
[7:12] Loading spectral library D:\Users_FPL\2025_DIANN\20250430_Green_Monkey\report-lib.predicted.speclib
[7:18] Library annotated with sequence database(s): camprotR_240512_cRAP_20190401_full_tags.fasta; D:\Users_FPL\2025_DIANN\20250430_Green_Monkey\UP000029965_60711_Green_Monkey.fasta
[7:19] Spectral library loaded: 19250 protein isoforms, 26632 protein groups and 4057560 precursors in 1262959 elution groups.
[7:19] Loading protein annotations from FASTA camprotR_240512_cRAP_20190401_full_tags.fasta
[7:19] Loading protein annotations from FASTA D:\Users_FPL\2025_DIANN\20250430_Green_Monkey\UP000029965_60711_Green_Monkey.fasta
[7:20] Annotating library proteins with information from the FASTA database
[7:20] Gene names missing for some isoforms
[7:20] Library contains 19250 proteins, and 16585 genes
[7:24] Initialising library

First pass: generating a spectral library from DIA data

[7:45] File #1/15
[7:45] Loading run \\BackupMS4\BackupMS4\MS Raw Files\2025\SCP\20251104_WY_Vero\20250411_S1_WT_1_S1-C1_1_6641.d
WARNING: for the vast majority of timsTOF datasets it is better to manually fix both the MS1 and MS2 mass accuracies to 15 ppm
[8:15] Pre-processing...
[8:55] 1068 MS1 and 34148 MS2 scans in 1068 (inferred) and 1068 (encoded) cycles, 2848122 precursors in range
[8:58] Calibrating with mass accuracies 30 (MS1), 22 (MS2)
[12:15] RT window set to 1.47699
[12:15] IM window set to 0.0419993
[12:15] Peak width: 2.656
[12:15] Scan window radius set to 5
[12:15] Recommended MS1 mass accuracy setting: 7 ppm
[16:44] Optimised mass accuracy: 10 ppm
[18:23] Main search
[20:16] Removing low confidence identifications
[20:19] Removing interfering precursors
[20:25] Training neural networks on 13180 target and 6985 decoy PSMs
[20:33] Number of IDs at 0.01 FDR: 4814
[20:35] Calculating protein q-values
[20:35] Number of genes identified at 1% FDR: 662 (precursor-level), 563 (protein-level) (inference performed using proteotypic peptides only)
[20:35] Quantification
[20:37] Quantification information saved to \\BackupMS4\BackupMS4\MS Raw Files\2025\SCP\20251104_WY_Vero\20250411_S1_WT_1_S1-C1_1_6641.d.quant

[20:37] File #2/15
[20:37] Loading run \\BackupMS4\BackupMS4\MS Raw Files\2025\SCP\20251104_WY_Vero\20250411_S2_WT_2_S1-C2_1_6642.d
[21:08] Pre-processing...
[21:51] 1068 MS1 and 34148 MS2 scans in 1068 (inferred) and 1068 (encoded) cycles, 2848122 precursors in range
[21:54] Calibrating with mass accuracies 30 (MS1), 22 (MS2)
[25:55] RT window set to 1.31964
[25:55] IM window set to 0.0467232
[25:55] Recommended MS1 mass accuracy setting: 8 ppm
[28:01] Main search
[29:56] Removing low confidence identifications
[29:59] Removing interfering precursors
[30:04] Training neural networks on 12475 target and 6772 decoy PSMs
[30:12] Number of IDs at 0.01 FDR: 4215
[30:14] Calculating protein q-values
[30:14] Number of genes identified at 1% FDR: 617 (precursor-level), 521 (protein-level) (inference performed using proteotypic peptides only)
[30:14] Quantification
[30:16] Quantification information saved to \\BackupMS4\BackupMS4\MS Raw Files\2025\SCP\20251104_WY_Vero\20250411_S2_WT_2_S1-C2_1_6642.d.quant

[30:16] File #3/15
[30:16] Loading run \\BackupMS4\BackupMS4\MS Raw Files\2025\SCP\20251104_WY_Vero\20250411_S3_WT_3_S1-C3_1_6643.d
[30:46] Pre-processing...
[31:26] 1068 MS1 and 34150 MS2 scans in 1068 (inferred) and 1068 (encoded) cycles, 2848122 precursors in range
[31:29] Calibrating with mass accuracies 30 (MS1), 23 (MS2)
[37:00] RT window set to 1.30759
[37:00] IM window set to 0.0464535
[37:01] Recommended MS1 mass accuracy setting: 7 ppm
[39:00] Main search
[40:48] Removing low confidence identifications
[40:51] Removing interfering precursors
[40:56] Training neural networks on 11589 target and 6423 decoy PSMs
[41:04] Number of IDs at 0.01 FDR: 3972
[41:05] Calculating protein q-values
[41:06] Number of genes identified at 1% FDR: 567 (precursor-level), 474 (protein-level) (inference performed using proteotypic peptides only)
[41:06] Quantification
[41:08] Quantification information saved to \\BackupMS4\BackupMS4\MS Raw Files\2025\SCP\20251104_WY_Vero\20250411_S3_WT_3_S1-C3_1_6643.d.quant

[41:08] File #4/15
[41:08] Loading run \\BackupMS4\BackupMS4\MS Raw Files\2025\SCP\20251104_WY_Vero\20250411_S4_A_1_S1-C4_1_6644.d
[41:38] Pre-processing...
[42:19] 1068 MS1 and 34150 MS2 scans in 1068 (inferred) and 1068 (encoded) cycles, 2848122 precursors in range
[42:22] Calibrating with mass accuracies 30 (MS1), 22 (MS2)
[47:57] RT window set to 1.35009
[47:57] IM window set to 0.0469329
[47:58] Recommended MS1 mass accuracy setting: 7 ppm
[50:05] Main search
[52:02] Removing low confidence identifications
[52:05] Removing interfering precursors
[52:10] Training neural networks on 13353 target and 7168 decoy PSMs
[52:19] Number of IDs at 0.01 FDR: 4434
[52:20] Calculating protein q-values
[52:20] Number of genes identified at 1% FDR: 669 (precursor-level), 569 (protein-level) (inference performed using proteotypic peptides only)
[52:20] Quantification
[52:22] Quantification information saved to \\BackupMS4\BackupMS4\MS Raw Files\2025\SCP\20251104_WY_Vero\20250411_S4_A_1_S1-C4_1_6644.d.quant

[52:22] File #5/15
[52:22] Loading run \\BackupMS4\BackupMS4\MS Raw Files\2025\SCP\20251104_WY_Vero\20250411_S5_A_2_S1-C5_1_6645.d
[52:52] Pre-processing...
[53:35] 1068 MS1 and 34152 MS2 scans in 1068 (inferred) and 1068 (encoded) cycles, 2848122 precursors in range
[53:38] Calibrating with mass accuracies 30 (MS1), 22 (MS2)
[59:17] RT window set to 1.63468
[59:17] IM window set to 0.0409003
[59:17] Recommended MS1 mass accuracy setting: 8 ppm
[61:57] Main search
[64:12] Removing low confidence identifications
[64:15] Removing interfering precursors
[64:20] Training neural networks on 10181 target and 5697 decoy PSMs
[64:29] Number of IDs at 0.01 FDR: 3222
[64:30] Calculating protein q-values
[64:30] Number of genes identified at 1% FDR: 468 (precursor-level), 411 (protein-level) (inference performed using proteotypic peptides only)
[64:30] Quantification
[64:32] Quantification information saved to \\BackupMS4\BackupMS4\MS Raw Files\2025\SCP\20251104_WY_Vero\20250411_S5_A_2_S1-C5_1_6645.d.quant

[64:32] File #6/15
[64:32] Loading run \\BackupMS4\BackupMS4\MS Raw Files\2025\SCP\20251104_WY_Vero\20250411_S6_A_3_S1-C6_1_6646.d
[65:02] Pre-processing...
[65:41] 1068 MS1 and 34150 MS2 scans in 1068 (inferred) and 1068 (encoded) cycles, 2848122 precursors in range
[65:44] Calibrating with mass accuracies 30 (MS1), 24 (MS2)
[70:20] RT window set to 1.9319
[70:20] IM window set to 0.0456707
[70:20] Recommended MS1 mass accuracy setting: 7 ppm
[72:40] Main search
[75:02] Removing low confidence identifications
[75:05] Removing interfering precursors
[75:10] Training neural networks on 10492 target and 5454 decoy PSMs
[75:18] Number of IDs at 0.01 FDR: 3548
[75:19] Calculating protein q-values
[75:19] Number of genes identified at 1% FDR: 523 (precursor-level), 448 (protein-level) (inference performed using proteotypic peptides only)
[75:19] Quantification
[75:21] Quantification information saved to \\BackupMS4\BackupMS4\MS Raw Files\2025\SCP\20251104_WY_Vero\20250411_S6_A_3_S1-C6_1_6646.d.quant

[75:21] File #7/15
[75:21] Loading run \\BackupMS4\BackupMS4\MS Raw Files\2025\SCP\20251104_WY_Vero\20250411_S7_B_1_S1-C7_1_6647.d
[75:52] Pre-processing...
[76:30] 1068 MS1 and 34150 MS2 scans in 1068 (inferred) and 1068 (encoded) cycles, 2848122 precursors in range
[76:33] Calibrating with mass accuracies 30 (MS1), 21 (MS2)
[82:53] RT window set to 1.66326
[82:53] IM window set to 0.0438572
[82:54] Recommended MS1 mass accuracy setting: 7 ppm
[85:00] Main search
[86:57] Removing low confidence identifications
[87:00] Removing interfering precursors
[87:05] Training neural networks on 10481 target and 5514 decoy PSMs
[87:13] Number of IDs at 0.01 FDR: 3557
[87:14] Calculating protein q-values
[87:14] Number of genes identified at 1% FDR: 556 (precursor-level), 487 (protein-level) (inference performed using proteotypic peptides only)
[87:14] Quantification
[87:16] Quantification information saved to \\BackupMS4\BackupMS4\MS Raw Files\2025\SCP\20251104_WY_Vero\20250411_S7_B_1_S1-C7_1_6647.d.quant

[87:16] File #8/15
[87:16] Loading run \\BackupMS4\BackupMS4\MS Raw Files\2025\SCP\20251104_WY_Vero\20250411_S8_B_2_S1-C8_1_6648.d
[87:46] Pre-processing...
[88:25] 1068 MS1 and 34150 MS2 scans in 1068 (inferred) and 1068 (encoded) cycles, 2848122 precursors in range
[88:28] Calibrating with mass accuracies 30 (MS1), 23 (MS2)
[93:01] RT window set to 1.74394
[93:01] IM window set to 0.0428342
[93:02] Recommended MS1 mass accuracy setting: 7 ppm
[95:18] Main search
[97:32] Removing low confidence identifications
[97:35] Removing interfering precursors
[97:40] Training neural networks on 9865 target and 4878 decoy PSMs
[97:48] Number of IDs at 0.01 FDR: 4251
[97:49] Calculating protein q-values
[97:50] Number of genes identified at 1% FDR: 620 (precursor-level), 532 (protein-level) (inference performed using proteotypic peptides only)
[97:50] Quantification
[97:52] Quantification information saved to \\BackupMS4\BackupMS4\MS Raw Files\2025\SCP\20251104_WY_Vero\20250411_S8_B_2_S1-C8_1_6648.d.quant

[97:52] File #9/15
[97:52] Loading run \\BackupMS4\BackupMS4\MS Raw Files\2025\SCP\20251104_WY_Vero\20250411_S9_B_3_S1-C9_1_6649.d
[98:22] Pre-processing...
[98:59] 1068 MS1 and 34150 MS2 scans in 1068 (inferred) and 1068 (encoded) cycles, 2848122 precursors in range
[99:02] Calibrating with mass accuracies 30 (MS1), 24 (MS2)
[106:36] RT window set to 1.84422
[106:36] IM window set to 0.0443344
[106:36] Recommended MS1 mass accuracy setting: 7 ppm
[109:35] Main search
[111:33] Removing low confidence identifications
[111:36] Removing interfering precursors
[111:41] Training neural networks on 7929 target and 3929 decoy PSMs
[111:49] Number of IDs at 0.01 FDR: 2704
[111:50] Calculating protein q-values
[111:50] Number of genes identified at 1% FDR: 447 (precursor-level), 379 (protein-level) (inference performed using proteotypic peptides only)
[111:50] Quantification
[111:52] Quantification information saved to \\BackupMS4\BackupMS4\MS Raw Files\2025\SCP\20251104_WY_Vero\20250411_S9_B_3_S1-C9_1_6649.d.quant

[111:52] File #10/15
[111:52] Loading run \\BackupMS4\BackupMS4\MS Raw Files\2025\SCP\20251104_WY_Vero\20250411_S10_Teth_1_S1-B7_1_6638.d
[112:23] Pre-processing...
[113:00] 1068 MS1 and 34150 MS2 scans in 1068 (inferred) and 1068 (encoded) cycles, 2848122 precursors in range
[113:03] Calibrating with mass accuracies 30 (MS1), 23 (MS2)
[117:29] RT window set to 1.62563
[117:29] IM window set to 0.0446048
[117:29] Recommended MS1 mass accuracy setting: 7 ppm
[119:29] Main search
[121:23] Removing low confidence identifications
[121:26] Removing interfering precursors
[121:32] Training neural networks on 13158 target and 6673 decoy PSMs
[121:40] Number of IDs at 0.01 FDR: 4804
[121:41] Calculating protein q-values
[121:42] Number of genes identified at 1% FDR: 695 (precursor-level), 633 (protein-level) (inference performed using proteotypic peptides only)
[121:42] Quantification
[121:44] Quantification information saved to \\BackupMS4\BackupMS4\MS Raw Files\2025\SCP\20251104_WY_Vero\20250411_S10_Teth_1_S1-B7_1_6638.d.quant

[121:44] File #11/15
[121:44] Loading run \\BackupMS4\BackupMS4\MS Raw Files\2025\SCP\20251104_WY_Vero\20250411_S11_Teth_2_S1-B8_1_6639.d
[122:14] Pre-processing...
[122:51] 1068 MS1 and 34148 MS2 scans in 1068 (inferred) and 1068 (encoded) cycles, 2848122 precursors in range
[122:54] Calibrating with mass accuracies 30 (MS1), 24 (MS2)
[126:35] RT window set to 1.66122
[126:35] IM window set to 0.0417736
[126:35] Recommended MS1 mass accuracy setting: 7 ppm
[128:34] Main search
[130:26] Removing low confidence identifications
[130:29] Removing interfering precursors
[130:35] Training neural networks on 15444 target and 8434 decoy PSMs
[130:44] Number of IDs at 0.01 FDR: 5167
[130:45] Calculating protein q-values
[130:46] Number of genes identified at 1% FDR: 756 (precursor-level), 656 (protein-level) (inference performed using proteotypic peptides only)
[130:46] Quantification
[130:48] Quantification information saved to \\BackupMS4\BackupMS4\MS Raw Files\2025\SCP\20251104_WY_Vero\20250411_S11_Teth_2_S1-B8_1_6639.d.quant

[130:48] File #12/15
[130:48] Loading run \\BackupMS4\BackupMS4\MS Raw Files\2025\SCP\20251104_WY_Vero\20250411_S12_Teth_3_S1-B9_1_6640.d
[131:18] Pre-processing...
[131:56] 1068 MS1 and 34150 MS2 scans in 1068 (inferred) and 1068 (encoded) cycles, 2848122 precursors in range
[131:59] Calibrating with mass accuracies 30 (MS1), 21 (MS2)
[136:24] RT window set to 1.24506
[136:24] IM window set to 0.0433254
[136:24] Recommended MS1 mass accuracy setting: 6 ppm
[138:25] Main search
[139:52] Removing low confidence identifications
[139:55] Removing interfering precursors
[140:00] Training neural networks on 13299 target and 6931 decoy PSMs
[140:08] Number of IDs at 0.01 FDR: 4651
[140:10] Calculating protein q-values
[140:10] Number of genes identified at 1% FDR: 648 (precursor-level), 516 (protein-level) (inference performed using proteotypic peptides only)
[140:10] Quantification
[140:12] Quantification information saved to \\BackupMS4\BackupMS4\MS Raw Files\2025\SCP\20251104_WY_Vero\20250411_S12_Teth_3_S1-B9_1_6640.d.quant

[140:12] File #13/15
[140:12] Loading run \\BackupMS4\BackupMS4\MS Raw Files\2025\SCP\20251104_WY_Vero\20250411_S13_AB_1_S1-C10_1_6652.d
[140:43] Pre-processing...
[141:29] 1068 MS1 and 34148 MS2 scans in 1068 (inferred) and 1068 (encoded) cycles, 2848122 precursors in range
[141:32] Calibrating with mass accuracies 30 (MS1), 24 (MS2)
[151:06] RT window set to 2.20527
[151:06] IM window set to 0.0443237
[151:07] Recommended MS1 mass accuracy setting: 7 ppm
[155:17] Main search
[157:38] Removing low confidence identifications
[157:40] Removing interfering precursors
[157:46] Training neural networks on 7353 target and 4017 decoy PSMs
[157:52] Number of IDs at 0.01 FDR: 2094
[157:54] Calculating protein q-values
[157:54] Number of genes identified at 1% FDR: 367 (precursor-level), 322 (protein-level) (inference performed using proteotypic peptides only)
[157:54] Quantification
[157:56] Quantification information saved to \\BackupMS4\BackupMS4\MS Raw Files\2025\SCP\20251104_WY_Vero\20250411_S13_AB_1_S1-C10_1_6652.d.quant

[157:56] File #14/15
[157:56] Loading run \\BackupMS4\BackupMS4\MS Raw Files\2025\SCP\20251104_WY_Vero\20250411_S14_AB_2_S1-C11_1_6653.d
[158:28] Pre-processing...
[159:16] 1068 MS1 and 34150 MS2 scans in 1068 (inferred) and 1068 (encoded) cycles, 2848122 precursors in range
[159:19] Calibrating with mass accuracies 30 (MS1), 25 (MS2)
[163:38] RT window set to 1.91393
[163:38] IM window set to 0.044014
[163:38] Recommended MS1 mass accuracy setting: 8 ppm
[166:21] Main search
[169:00] Removing low confidence identifications
[169:03] Removing interfering precursors
[169:08] Training neural networks on 12066 target and 6295 decoy PSMs
[169:16] Number of IDs at 0.01 FDR: 4250
[169:17] Calculating protein q-values
[169:18] Number of genes identified at 1% FDR: 686 (precursor-level), 614 (protein-level) (inference performed using proteotypic peptides only)
[169:18] Quantification
[169:20] Quantification information saved to \\BackupMS4\BackupMS4\MS Raw Files\2025\SCP\20251104_WY_Vero\20250411_S14_AB_2_S1-C11_1_6653.d.quant

[169:20] File #15/15
[169:20] Loading run \\BackupMS4\BackupMS4\MS Raw Files\2025\SCP\20251104_WY_Vero\20250411_S15_AB_3_S1-C12_1_6654.d
[169:51] Pre-processing...
[170:37] 1068 MS1 and 34150 MS2 scans in 1068 (inferred) and 1068 (encoded) cycles, 2848122 precursors in range
[170:40] Calibrating with mass accuracies 30 (MS1), 25 (MS2)
[175:45] RT window set to 1.77484
[175:45] IM window set to 0.0448846
[175:45] Recommended MS1 mass accuracy setting: 8 ppm
[178:18] Main search
[180:50] Removing low confidence identifications
[180:52] Removing interfering precursors
[180:58] Training neural networks on 8613 target and 4174 decoy PSMs
[181:05] Number of IDs at 0.01 FDR: 3423
[181:06] Calculating protein q-values
[181:06] Number of genes identified at 1% FDR: 587 (precursor-level), 512 (protein-level) (inference performed using proteotypic peptides only)
[181:06] Quantification
[181:09] Quantification information saved to \\BackupMS4\BackupMS4\MS Raw Files\2025\SCP\20251104_WY_Vero\20250411_S15_AB_3_S1-C12_1_6654.d.quant

[181:09] Cross-run analysis
[181:09] Reading quantification information: 15 files
[181:23] Quantifying peptides
[181:38] Assembling protein groups
[181:40] Quantifying proteins
[181:40] Calculating q-values for protein and gene groups
[181:42] Calculating global q-values for protein and gene groups
[181:42] Protein groups with global q-value <= 0.01: 977
[181:43] Compressed report saved to D:\Users_FPL\2025_DIANN\20250430_Green_Monkey\report-first-pass.parquet. Use R 'arrow' or Python 'PyArrow' package to process
[181:43] Saving precursor levels matrix
[181:43] Precursor levels matrix (1% precursor and protein group FDR) saved to D:\Users_FPL\2025_DIANN\20250430_Green_Monkey\report-first-pass.pr_matrix.tsv.
[181:43] Manifest saved to D:\Users_FPL\2025_DIANN\20250430_Green_Monkey\report-first-pass.manifest.txt
[181:43] Stats report saved to D:\Users_FPL\2025_DIANN\20250430_Green_Monkey\report-first-pass.stats.tsv
[181:43] Generating spectral library:
[181:43] 8604 target and 86 decoy precursors saved
[181:43] Spectral library saved to D:\Users_FPL\2025_DIANN\20250430_Green_Monkey\report-lib.parquet

[181:44] Loading spectral library D:\Users_FPL\2025_DIANN\20250430_Green_Monkey\report-lib.parquet
[181:44] Spectral library loaded: 1228 protein isoforms, 1163 protein groups and 8690 precursors in 8303 elution groups.
[181:44] Loading protein annotations from FASTA camprotR_240512_cRAP_20190401_full_tags.fasta
[181:44] Loading protein annotations from FASTA D:\Users_FPL\2025_DIANN\20250430_Green_Monkey\UP000029965_60711_Green_Monkey.fasta
[181:45] Annotating library proteins with information from the FASTA database
[181:45] Gene names missing for some isoforms
[181:45] Library contains 1228 proteins, and 1112 genes
[181:45] Initialising library
[181:45] Saving the library to D:\Users_FPL\2025_DIANN\20250430_Green_Monkey\report-lib.parquet.skyline.speclib


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

[181:45] File #1/15
[181:45] Loading run \\BackupMS4\BackupMS4\MS Raw Files\2025\SCP\20251104_WY_Vero\20250411_S1_WT_1_S1-C1_1_6641.d
[182:15] Pre-processing...
[182:53] 1068 MS1 and 34148 MS2 scans in 1068 (inferred) and 1068 (encoded) cycles, 8604 precursors in range
[182:53] Calibrating with mass accuracies 30 (MS1), 21 (MS2)
[182:54] RT window set to 0.449857
[182:54] IM window set to 0.0116752
[182:54] Recommended MS1 mass accuracy setting: 9 ppm
[182:55] Main search
[182:55] Removing low confidence identifications
[182:56] Removing interfering precursors
[182:56] Training neural networks on 7836 target and 4128 decoy PSMs
[183:02] Number of IDs at 0.01 FDR: 6250
[183:02] Calculating protein q-values
[183:02] Number of genes identified at 1% FDR: 741 (precursor-level), 659 (protein-level) (inference performed using proteotypic peptides only)
[183:02] Quantification

[183:03] File #2/15
[183:03] Loading run \\BackupMS4\BackupMS4\MS Raw Files\2025\SCP\20251104_WY_Vero\20250411_S2_WT_2_S1-C2_1_6642.d
[183:35] Pre-processing...
[184:15] 1068 MS1 and 34148 MS2 scans in 1068 (inferred) and 1068 (encoded) cycles, 8604 precursors in range
[184:15] Calibrating with mass accuracies 30 (MS1), 22 (MS2)
[184:17] RT window set to 0.461456
[184:17] IM window set to 0.0140428
[184:17] Recommended MS1 mass accuracy setting: 10 ppm
[184:17] Main search
[184:18] Removing low confidence identifications
[184:18] Removing interfering precursors
[184:18] Training neural networks on 7825 target and 4691 decoy PSMs
[184:24] Number of IDs at 0.01 FDR: 5526
[184:24] Calculating protein q-values
[184:24] Number of genes identified at 1% FDR: 678 (precursor-level), 613 (protein-level) (inference performed using proteotypic peptides only)
[184:24] Quantification

[184:25] File #3/15
[184:25] Loading run \\BackupMS4\BackupMS4\MS Raw Files\2025\SCP\20251104_WY_Vero\20250411_S3_WT_3_S1-C3_1_6643.d
[184:56] Pre-processing...
[185:33] 1068 MS1 and 34150 MS2 scans in 1068 (inferred) and 1068 (encoded) cycles, 8604 precursors in range
[185:33] Calibrating with mass accuracies 30 (MS1), 22 (MS2)
[185:34] RT window set to 0.459102
[185:34] IM window set to 0.0133379
[185:34] Recommended MS1 mass accuracy setting: 9 ppm
[185:35] Main search
[185:35] Removing low confidence identifications
[185:36] Removing interfering precursors
[185:36] Training neural networks on 7780 target and 4578 decoy PSMs
[185:42] Number of IDs at 0.01 FDR: 5619
[185:42] Calculating protein q-values
[185:42] Number of genes identified at 1% FDR: 661 (precursor-level), 570 (protein-level) (inference performed using proteotypic peptides only)
[185:42] Quantification

[185:44] File #4/15
[185:44] Loading run \\BackupMS4\BackupMS4\MS Raw Files\2025\SCP\20251104_WY_Vero\20250411_S4_A_1_S1-C4_1_6644.d
[186:14] Pre-processing...
[186:52] 1068 MS1 and 34150 MS2 scans in 1068 (inferred) and 1068 (encoded) cycles, 8604 precursors in range
[186:52] Calibrating with mass accuracies 30 (MS1), 22 (MS2)
[186:54] RT window set to 0.463686
[186:54] IM window set to 0.0132212
[186:54] Recommended MS1 mass accuracy setting: 10 ppm
[186:55] Main search
[186:55] Removing low confidence identifications
[186:55] Removing interfering precursors
[186:55] Training neural networks on 7962 target and 4647 decoy PSMs
[187:01] Number of IDs at 0.01 FDR: 6022
[187:01] Calculating protein q-values
[187:01] Number of genes identified at 1% FDR: 731 (precursor-level), 647 (protein-level) (inference performed using proteotypic peptides only)
[187:01] Quantification

[187:03] File #5/15
[187:03] Loading run \\BackupMS4\BackupMS4\MS Raw Files\2025\SCP\20251104_WY_Vero\20250411_S5_A_2_S1-C5_1_6645.d
[187:33] Pre-processing...
[188:13] 1068 MS1 and 34152 MS2 scans in 1068 (inferred) and 1068 (encoded) cycles, 8604 precursors in range
[188:13] Calibrating with mass accuracies 30 (MS1), 22 (MS2)
[188:15] RT window set to 0.467666
[188:15] IM window set to 0.0158086
[188:15] Recommended MS1 mass accuracy setting: 10 ppm
[188:15] Main search
[188:16] Removing low confidence identifications
[188:16] Removing interfering precursors
[188:16] Training neural networks on 7719 target and 4706 decoy PSMs
[188:22] Number of IDs at 0.01 FDR: 4886
[188:22] Calculating protein q-values
[188:22] Number of genes identified at 1% FDR: 617 (precursor-level), 553 (protein-level) (inference performed using proteotypic peptides only)
[188:22] Quantification

[188:24] File #6/15
[188:24] Loading run \\BackupMS4\BackupMS4\MS Raw Files\2025\SCP\20251104_WY_Vero\20250411_S6_A_3_S1-C6_1_6646.d
[188:53] Pre-processing...
[189:29] 1068 MS1 and 34150 MS2 scans in 1068 (inferred) and 1068 (encoded) cycles, 8604 precursors in range
[189:29] Calibrating with mass accuracies 30 (MS1), 23 (MS2)
[189:31] RT window set to 0.462784
[189:31] IM window set to 0.0132911
[189:31] Recommended MS1 mass accuracy setting: 10 ppm
[189:32] Main search
[189:32] Removing low confidence identifications
[189:32] Removing interfering precursors
[189:32] Training neural networks on 7750 target and 4302 decoy PSMs
[189:38] Number of IDs at 0.01 FDR: 5409
[189:38] Calculating protein q-values
[189:38] Number of genes identified at 1% FDR: 659 (precursor-level), 605 (protein-level) (inference performed using proteotypic peptides only)
[189:38] Quantification

[189:40] File #7/15
[189:40] Loading run \\BackupMS4\BackupMS4\MS Raw Files\2025\SCP\20251104_WY_Vero\20250411_S7_B_1_S1-C7_1_6647.d
[190:09] Pre-processing...
[190:45] 1068 MS1 and 34150 MS2 scans in 1068 (inferred) and 1068 (encoded) cycles, 8604 precursors in range
[190:45] Calibrating with mass accuracies 30 (MS1), 21 (MS2)
[190:47] RT window set to 0.456159
[190:47] IM window set to 0.0137544
[190:47] Recommended MS1 mass accuracy setting: 10 ppm
[190:48] Main search
[190:48] Removing low confidence identifications
[190:48] Removing interfering precursors
[190:48] Training neural networks on 7726 target and 4032 decoy PSMs
[190:54] Number of IDs at 0.01 FDR: 5091
[190:54] Calculating protein q-values
[190:54] Number of genes identified at 1% FDR: 651 (precursor-level), 591 (protein-level) (inference performed using proteotypic peptides only)
[190:54] Quantification

[190:55] File #8/15
[190:55] Loading run \\BackupMS4\BackupMS4\MS Raw Files\2025\SCP\20251104_WY_Vero\20250411_S8_B_2_S1-C8_1_6648.d
[191:25] Pre-processing...
[192:01] 1068 MS1 and 34150 MS2 scans in 1068 (inferred) and 1068 (encoded) cycles, 8604 precursors in range
[192:01] Calibrating with mass accuracies 30 (MS1), 22 (MS2)
[192:03] RT window set to 0.461396
[192:03] IM window set to 0.0129112
[192:03] Recommended MS1 mass accuracy setting: 9 ppm
[192:03] Main search
[192:04] Removing low confidence identifications
[192:04] Removing interfering precursors
[192:04] Training neural networks on 7744 target and 4363 decoy PSMs
[192:10] Number of IDs at 0.01 FDR: 5769
[192:10] Calculating protein q-values
[192:10] Number of genes identified at 1% FDR: 721 (precursor-level), 632 (protein-level) (inference performed using proteotypic peptides only)
[192:10] Quantification

[192:12] File #9/15
[192:12] Loading run \\BackupMS4\BackupMS4\MS Raw Files\2025\SCP\20251104_WY_Vero\20250411_S9_B_3_S1-C9_1_6649.d
[192:42] Pre-processing...
[193:17] 1068 MS1 and 34150 MS2 scans in 1068 (inferred) and 1068 (encoded) cycles, 8604 precursors in range
[193:17] Calibrating with mass accuracies 30 (MS1), 22 (MS2)
[193:18] RT window set to 0.465739
[193:18] IM window set to 0.0131814
[193:18] Recommended MS1 mass accuracy setting: 10 ppm
[193:19] Main search
[193:19] Removing low confidence identifications
[193:19] Removing interfering precursors
[193:19] Training neural networks on 7305 target and 3375 decoy PSMs
[193:25] Number of IDs at 0.01 FDR: 4762
[193:25] Calculating protein q-values
[193:25] Number of genes identified at 1% FDR: 621 (precursor-level), 524 (protein-level) (inference performed using proteotypic peptides only)
[193:25] Quantification

[193:26] File #10/15
[193:26] Loading run \\BackupMS4\BackupMS4\MS Raw Files\2025\SCP\20251104_WY_Vero\20250411_S10_Teth_1_S1-B7_1_6638.d
[193:56] Pre-processing...
[194:31] 1068 MS1 and 34150 MS2 scans in 1068 (inferred) and 1068 (encoded) cycles, 8604 precursors in range
[194:31] Calibrating with mass accuracies 30 (MS1), 22 (MS2)
[194:32] RT window set to 0.445377
[194:32] IM window set to 0.0130961
[194:32] Recommended MS1 mass accuracy setting: 10 ppm
[194:33] Main search
[194:33] Removing low confidence identifications
[194:34] Removing interfering precursors
[194:34] Training neural networks on 7835 target and 4049 decoy PSMs
[194:39] Number of IDs at 0.01 FDR: 6249
[194:40] Calculating protein q-values
[194:40] Number of genes identified at 1% FDR: 763 (precursor-level), 673 (protein-level) (inference performed using proteotypic peptides only)
[194:40] Quantification

[194:41] File #11/15
[194:41] Loading run \\BackupMS4\BackupMS4\MS Raw Files\2025\SCP\20251104_WY_Vero\20250411_S11_Teth_2_S1-B8_1_6639.d
[195:11] Pre-processing...
[195:45] 1068 MS1 and 34148 MS2 scans in 1068 (inferred) and 1068 (encoded) cycles, 8604 precursors in range
[195:45] Calibrating with mass accuracies 30 (MS1), 22 (MS2)
[195:47] RT window set to 0.437827
[195:47] IM window set to 0.013547
[195:47] Recommended MS1 mass accuracy setting: 9 ppm
[195:48] Main search
[195:48] Removing low confidence identifications
[195:48] Removing interfering precursors
[195:48] Training neural networks on 7724 target and 4112 decoy PSMs
[195:54] Number of IDs at 0.01 FDR: 6338
[195:54] Calculating protein q-values
[195:54] Number of genes identified at 1% FDR: 755 (precursor-level), 643 (protein-level) (inference performed using proteotypic peptides only)
[195:54] Quantification

[195:56] File #12/15
[195:56] Loading run \\BackupMS4\BackupMS4\MS Raw Files\2025\SCP\20251104_WY_Vero\20250411_S12_Teth_3_S1-B9_1_6640.d
[196:26] Pre-processing...
[197:01] 1068 MS1 and 34150 MS2 scans in 1068 (inferred) and 1068 (encoded) cycles, 8604 precursors in range
[197:01] Calibrating with mass accuracies 30 (MS1), 22 (MS2)
[197:03] RT window set to 0.453264
[197:03] IM window set to 0.0128125
[197:03] Recommended MS1 mass accuracy setting: 10 ppm
[197:03] Main search
[197:04] Removing low confidence identifications
[197:04] Removing interfering precursors
[197:04] Training neural networks on 7724 target and 3745 decoy PSMs
[197:10] Number of IDs at 0.01 FDR: 5803
[197:10] Calculating protein q-values
[197:10] Number of genes identified at 1% FDR: 715 (precursor-level), 641 (protein-level) (inference performed using proteotypic peptides only)
[197:10] Quantification

[197:11] File #13/15
[197:11] Loading run \\BackupMS4\BackupMS4\MS Raw Files\2025\SCP\20251104_WY_Vero\20250411_S13_AB_1_S1-C10_1_6652.d
[197:42] Pre-processing...
[198:26] 1068 MS1 and 34148 MS2 scans in 1068 (inferred) and 1068 (encoded) cycles, 8604 precursors in range
[198:26] Calibrating with mass accuracies 30 (MS1), 23 (MS2)
[198:27] RT window set to 0.450119
[198:27] IM window set to 0.014942
[198:27] Recommended MS1 mass accuracy setting: 10 ppm
[198:28] Main search
[198:28] Removing low confidence identifications
[198:28] Removing interfering precursors
[198:28] Training neural networks on 6431 target and 2620 decoy PSMs
[198:34] Number of IDs at 0.01 FDR: 3228
[198:34] Calculating protein q-values
[198:34] Number of genes identified at 1% FDR: 494 (precursor-level), 446 (protein-level) (inference performed using proteotypic peptides only)
[198:34] Quantification

[198:36] File #14/15
[198:36] Loading run \\BackupMS4\BackupMS4\MS Raw Files\2025\SCP\20251104_WY_Vero\20250411_S14_AB_2_S1-C11_1_6653.d
[199:08] Pre-processing...
[199:53] 1068 MS1 and 34150 MS2 scans in 1068 (inferred) and 1068 (encoded) cycles, 8604 precursors in range
[199:53] Calibrating with mass accuracies 30 (MS1), 24 (MS2)
[199:55] RT window set to 0.451386
[199:55] IM window set to 0.0135692
[199:55] Recommended MS1 mass accuracy setting: 10 ppm
[199:55] Main search
[199:56] Removing low confidence identifications
[199:56] Removing interfering precursors
[199:56] Training neural networks on 7563 target and 4219 decoy PSMs
[200:02] Number of IDs at 0.01 FDR: 5520
[200:02] Calculating protein q-values
[200:02] Number of genes identified at 1% FDR: 711 (precursor-level), 616 (protein-level) (inference performed using proteotypic peptides only)
[200:02] Quantification

[200:04] File #15/15
[200:04] Loading run \\BackupMS4\BackupMS4\MS Raw Files\2025\SCP\20251104_WY_Vero\20250411_S15_AB_3_S1-C12_1_6654.d
[200:34] Pre-processing...
[201:18] 1068 MS1 and 34150 MS2 scans in 1068 (inferred) and 1068 (encoded) cycles, 8604 precursors in range
[201:18] Calibrating with mass accuracies 30 (MS1), 24 (MS2)
[201:20] RT window set to 0.435675
[201:20] IM window set to 0.013405
[201:20] Recommended MS1 mass accuracy setting: 10 ppm
[201:21] Main search
[201:21] Removing low confidence identifications
[201:21] Removing interfering precursors
[201:21] Training neural networks on 7394 target and 4233 decoy PSMs
[201:27] Number of IDs at 0.01 FDR: 4986
[201:27] Calculating protein q-values
[201:27] Number of genes identified at 1% FDR: 682 (precursor-level), 579 (protein-level) (inference performed using proteotypic peptides only)
[201:27] Quantification

[201:29] Cross-run analysis
[201:29] Reading quantification information: 15 files
[201:29] Quantifying peptides
[201:47] Quantification parameters: 0.369982, 0.00396148, 0.0149753, 0.0142927, 0.245756, 0.0931529, 0.290076, 0.260729, 0.30274, 0.0152483, 0.0143035, 0.0131169, 0.760918, 0.510572, 0.495045, 0.0919643
[201:55] Quantifying proteins
[201:55] Calculating q-values for protein and gene groups
[201:55] Calculating global q-values for protein and gene groups
[201:55] Protein groups with global q-value <= 0.01: 880
[201:56] Compressed report saved to D:\Users_FPL\2025_DIANN\20250430_Green_Monkey\report.parquet. Use R 'arrow' or Python 'PyArrow' package to process
[201:56] Saving precursor levels matrix
[201:56] Precursor levels matrix (1% precursor and protein group FDR) saved to D:\Users_FPL\2025_DIANN\20250430_Green_Monkey\report.pr_matrix.tsv.
[201:56] Saving protein group levels matrix
[201:56] Protein groups matrix saved to D:\Users_FPL\2025_DIANN\20250430_Green_Monkey\report.pg_matrix.tsv.
[201:56] Saving gene group levels matrix
[201:56] Gene groups matrix saved to D:\Users_FPL\2025_DIANN\20250430_Green_Monkey\report.gg_matrix.tsv.
[201:56] Saving unique genes levels matrix
[201:56] Unique genes matrix saved to D:\Users_FPL\2025_DIANN\20250430_Green_Monkey\report.unique_genes_matrix.tsv.
[201:56] Manifest saved to D:\Users_FPL\2025_DIANN\20250430_Green_Monkey\report.manifest.txt
[201:56] Stats report saved to D:\Users_FPL\2025_DIANN\20250430_Green_Monkey\report.stats.tsv