DIA-NN 1.8.1 (Data-Independent Acquisition by Neural Networks)
Compiled on Apr 14 2022 15:31:19
Current date and time: Fri May  5 13:49:49 2023
CPU: GenuineIntel Intel(R) Xeon(R) Gold 6226R CPU @ 2.90GHz
SIMD instructions: AVX AVX2 AVX512CD AVX512F FMA SSE4.1 SSE4.2 
Logical CPU cores: 64
diann.exe 
--f D:\Experiment_1+1_T-A5_1_14498.d  
--f D:\Experiment_1+1_T-A6_1_14499.d  
--f D:\Experiment_1+2_T-A7_1_14500.d  
--f D:\Experiment_1+2_T-A8_1_14501.d  
--f D:\Experiment_1-1_T-A1_1_14494.d  
--f D:\Experiment_1-1_T-A2_1_14495.d  
--f D:\Experiment_1-2_T-A3_1_14496.d  
--f D:\Experiment_1-2_T-A4_1_14497.d  
--f D:\Experiment_2+1_T-B1_1_14506.d  
--f D:\Experiment_2+1_T-B2_1_14507.d  
--f D:\Experiment_2+2_T-B3_1_14508.d  
--f D:\Experiment_2+2_T-B4_1_14509.d  
--f D:\Experiment_2-1_T-A9_1_14502.d  
--f D:\Experiment_2-1_T-A10_1_14503.d  
--f D:\Experiment_2-2_T-A11_1_14504.d  
--f D:\Experiment_2-2_T-A12_1_14505.d  
--f D:\Experiment_3+1_T-B9_1_14514.d  
--f D:\Experiment_3+1_T-B10_1_14515.d  
--f D:\Experiment_3+2_T-B11_1_14516.d  
--f D:\Experiment_3+2_T-B12_1_14517.d  
--f D:\Experiment_3-1_T-B5_1_14510.d  
--f D:\Experiment_3-1_T-B6_1_14511.d  
--f D:\Experiment_3-2_T-B7_1_14512.d  
--f D:\Experiment_3-2_T-B8_1_14513.d  
--lib  --threads 40 --verbose 1 --out D:\MassSpecData\2023\May23\RSharma\report.tsv --qvalue 0.01 --matrices --out-lib D:\MassSpecData\2023\May23\RSharma\lib.tsv --gen-spec-lib --predictor 
--fasta D:\FastaDatabases\KWynne\Human\uniprot-download_true_format_fasta_includeIsoform_true_query__28Huma-2023.01.27-16.04.15.82.fasta 
--fasta-search --min-fr-mz 200 --max-fr-mz 1800 --met-excision --cut K*,R* --missed-cleavages 1 --min-pep-len 6 --max-pep-len 30 --min-pr-mz 300 --max-pr-mz 1800 --min-pr-charge 2 --max-pr-charge 4 --unimod4 --double-search --reanalyse --relaxed-prot-inf --smart-profiling --pg-level 1 --peak-center --no-ifs-removal 

Thread number set to 40
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-free search enabled
Min fragment m/z set to 200
Max fragment m/z set to 1800
N-terminal methionine excision enabled
In silico digest will involve cuts at K*,R*
Maximum number of missed cleavages set to 1
Min peptide length set to 6
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 2
Max precursor charge set to 4
Cysteine carbamidomethylation enabled as a fixed modification
Neural networks will be used for peak selection
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
Highly heuristic protein grouping will be used, to reduce the number of protein groups obtained; this mode is recommended for benchmarking protein ID numbers; use with caution for anything else
When generating a spectral library, in silico predicted spectra will be retained if deemed more reliable than experimental ones
Implicit protein grouping: protein names; this determines which peptides are considered 'proteotypic' and thus affects protein FDR calculation
Fixed-width center of each elution peak will be used for quantification
Interference removal from fragment elution curves disabled
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.
Exclusion of fragments shared between heavy and light peptides from quantification is not supported in FASTA digest mode - disabled; to enable, generate an in silico predicted spectral library and analyse with this library

24 files will be processed
[0:00] Loading FASTA D:\FastaDatabases\KWynne\Human\uniprot-download_true_format_fasta_includeIsoform_true_query__28Huma-2023.01.27-16.04.15.82.fasta
[0:07] Processing FASTA
[0:14] Assembling elution groups
[0:22] 3667368 precursors generated
[0:22] Gene names missing for some isoforms
[0:22] Library contains 20381 proteins, and 20164 genes
[0:23] [0:29] [14:33] [17:14] [17:18] [17:22] Saving the library to D:\MassSpecData\2023\May23\RSharma\lib.predicted.speclib
[17:27] Initialising library

[17:31] First pass: generating a spectral library from DIA data
[17:31] File #1/24
[17:31] Loading run D:\Experiment_1+1_T-A5_1_14498.d
For most diaPASEF datasets it is better to manually fix both the MS1 and MS2 mass accuracies to values in the range 10-15 ppm.
[18:01] 2865545 library precursors are potentially detectable
[18:01] Processing...
[22:06] RT window set to 2.33108
[22:06] Ion mobility window set to 0.0405462
[22:06] Peak width: 6.852
[22:06] Scan window radius set to 15
[22:06] Recommended MS1 mass accuracy setting: 15.3683 ppm
[27:26] Optimised mass accuracy: 11.2686 ppm
[35:24] Removing low confidence identifications
[35:24] Removing interfering precursors
[35:27] Training neural networks: 55320 targets, 31613 decoys
[35:30] Number of IDs at 0.01 FDR: 21704
[44:31] Removing low confidence identifications
[44:31] Removing interfering precursors
[44:34] Training neural networks: 65203 targets, 35323 decoys
[44:37] Number of IDs at 0.01 FDR: 23040
[44:38] Calculating protein q-values
[44:38] Number of proteins identified at 1% FDR: 3533 (precursor-level), 3172 (protein-level) (inference performed using proteotypic peptides only)
[44:38] Quantification
[44:40] Quantification information saved to D:\Experiment_1+1_T-A5_1_14498.d.quant.

[44:48] File #2/24
[44:48] Loading run D:\Experiment_1+1_T-A6_1_14499.d
[45:18] 2865545 library precursors are potentially detectable
[45:18] Processing...
[49:35] RT window set to 2.28321
[49:35] Ion mobility window set to 0.0391731
[49:35] Recommended MS1 mass accuracy setting: 14.9111 ppm
[57:41] Removing low confidence identifications
[57:41] Removing interfering precursors
[57:44] Training neural networks: 56683 targets, 32649 decoys
[57:46] Number of IDs at 0.01 FDR: 22172
[66:40] Removing low confidence identifications
[66:40] Removing interfering precursors
[66:43] Training neural networks: 64082 targets, 34828 decoys
[66:46] Number of IDs at 0.01 FDR: 23339
[66:47] Calculating protein q-values
[66:47] Number of proteins identified at 1% FDR: 3561 (precursor-level), 3216 (protein-level) (inference performed using proteotypic peptides only)
[66:47] Quantification
[66:49] Quantification information saved to D:\Experiment_1+1_T-A6_1_14499.d.quant.

[66:58] File #3/24
[66:58] Loading run D:\Experiment_1+2_T-A7_1_14500.d
[67:26] 2865545 library precursors are potentially detectable
[67:26] Processing...
[70:59] RT window set to 2.24864
[70:59] Ion mobility window set to 0.0398251
[70:59] Recommended MS1 mass accuracy setting: 15.3204 ppm
[78:22] Removing low confidence identifications
[78:23] Removing interfering precursors
[78:25] Training neural networks: 57869 targets, 32984 decoys
[78:28] Number of IDs at 0.01 FDR: 24358
[86:46] Removing low confidence identifications
[86:46] Removing interfering precursors
[86:49] Training neural networks: 69805 targets, 37531 decoys
[86:53] Number of IDs at 0.01 FDR: 25679
[86:53] Calculating protein q-values
[86:54] Number of proteins identified at 1% FDR: 3675 (precursor-level), 3242 (protein-level) (inference performed using proteotypic peptides only)
[86:54] Quantification
[86:56] Quantification information saved to D:\Experiment_1+2_T-A7_1_14500.d.quant.

[87:04] File #4/24
[87:04] Loading run D:\Experiment_1+2_T-A8_1_14501.d
[87:31] 2865545 library precursors are potentially detectable
[87:31] Processing...
[91:04] RT window set to 2.32811
[91:04] Ion mobility window set to 0.0410214
[91:04] Recommended MS1 mass accuracy setting: 14.8107 ppm
[98:40] Removing low confidence identifications
[98:40] Removing interfering precursors
[98:43] Training neural networks: 58072 targets, 32780 decoys
[98:46] Number of IDs at 0.01 FDR: 24013
[107:20] Removing low confidence identifications
[107:20] Removing interfering precursors
[107:23] Training neural networks: 68521 targets, 37068 decoys
[107:27] Number of IDs at 0.01 FDR: 25527
[107:27] Calculating protein q-values
[107:28] Number of proteins identified at 1% FDR: 3715 (precursor-level), 3335 (protein-level) (inference performed using proteotypic peptides only)
[107:28] Quantification
[107:30] Quantification information saved to D:\Experiment_1+2_T-A8_1_14501.d.quant.

[107:38] File #5/24
[107:38] Loading run D:\Experiment_1-1_T-A1_1_14494.d
[108:06] 2865545 library precursors are potentially detectable
[108:07] Processing...
[112:21] RT window set to 2.40119
[112:21] Ion mobility window set to 0.0417737
[112:21] Recommended MS1 mass accuracy setting: 14.8942 ppm
[121:21] Removing low confidence identifications
[121:22] Removing interfering precursors
[121:24] Training neural networks: 57569 targets, 33211 decoys
[121:27] Number of IDs at 0.01 FDR: 23333
[131:09] Removing low confidence identifications
[131:09] Removing interfering precursors
[131:13] Training neural networks: 71231 targets, 38717 decoys
[131:16] Number of IDs at 0.01 FDR: 25125
[131:16] Calculating protein q-values
[131:17] Number of proteins identified at 1% FDR: 3759 (precursor-level), 3339 (protein-level) (inference performed using proteotypic peptides only)
[131:17] Quantification
[131:19] Quantification information saved to D:\Experiment_1-1_T-A1_1_14494.d.quant.

[131:28] File #6/24
[131:28] Loading run D:\Experiment_1-1_T-A2_1_14495.d
[131:57] 2865545 library precursors are potentially detectable
[131:57] Processing...
[135:37] RT window set to 2.20638
[135:37] Ion mobility window set to 0.0388077
[135:37] Recommended MS1 mass accuracy setting: 15.0552 ppm
[143:15] Removing low confidence identifications
[143:15] Removing interfering precursors
[143:18] Training neural networks: 57445 targets, 32622 decoys
[143:20] Number of IDs at 0.01 FDR: 23353
[151:56] Removing low confidence identifications
[151:56] Removing interfering precursors
[151:59] Training neural networks: 66986 targets, 36475 decoys
[152:03] Number of IDs at 0.01 FDR: 24528
[152:03] Calculating protein q-values
[152:04] Number of proteins identified at 1% FDR: 3648 (precursor-level), 3258 (protein-level) (inference performed using proteotypic peptides only)
[152:04] Quantification
[152:06] Quantification information saved to D:\Experiment_1-1_T-A2_1_14495.d.quant.

[152:15] File #7/24
[152:15] Loading run D:\Experiment_1-2_T-A3_1_14496.d
[152:43] 2865545 library precursors are potentially detectable
[152:43] Processing...
[156:50] RT window set to 2.26532
[156:50] Ion mobility window set to 0.0405186
[156:50] Recommended MS1 mass accuracy setting: 14.8401 ppm
[164:32] Removing low confidence identifications
[164:32] Removing interfering precursors
[164:35] Training neural networks: 59138 targets, 33803 decoys
[164:38] Number of IDs at 0.01 FDR: 24092
[173:02] Removing low confidence identifications
[173:02] Removing interfering precursors
[173:06] Training neural networks: 70920 targets, 38380 decoys
[173:09] Number of IDs at 0.01 FDR: 25581
[173:10] Calculating protein q-values
[173:10] Number of proteins identified at 1% FDR: 3622 (precursor-level), 3220 (protein-level) (inference performed using proteotypic peptides only)
[173:10] Quantification
[173:12] Quantification information saved to D:\Experiment_1-2_T-A3_1_14496.d.quant.

[173:21] File #8/24
[173:21] Loading run D:\Experiment_1-2_T-A4_1_14497.d
[173:50] 2865545 library precursors are potentially detectable
[173:50] Processing...
[177:55] RT window set to 2.32921
[177:55] Ion mobility window set to 0.0380352
[177:56] Recommended MS1 mass accuracy setting: 14.2453 ppm
[185:42] Removing low confidence identifications
[185:42] Removing interfering precursors
[185:45] Training neural networks: 55402 targets, 31976 decoys
[185:48] Number of IDs at 0.01 FDR: 22289
[194:16] Removing low confidence identifications
[194:16] Removing interfering precursors
[194:19] Training neural networks: 65093 targets, 35595 decoys
[194:22] Number of IDs at 0.01 FDR: 23419
[194:23] Calculating protein q-values
[194:23] Number of proteins identified at 1% FDR: 3522 (precursor-level), 3164 (protein-level) (inference performed using proteotypic peptides only)
[194:23] Quantification
[194:25] Quantification information saved to D:\Experiment_1-2_T-A4_1_14497.d.quant.

[194:34] File #9/24
[194:34] Loading run D:\Experiment_2+1_T-B1_1_14506.d
[195:01] 2865545 library precursors are potentially detectable
[195:01] Processing...
[199:41] RT window set to 2.35025
[199:41] Ion mobility window set to 0.0409177
[199:41] Recommended MS1 mass accuracy setting: 15.0045 ppm
[207:27] Removing low confidence identifications
[207:27] Removing interfering precursors
[207:29] Training neural networks: 46034 targets, 25933 decoys
[207:32] Number of IDs at 0.01 FDR: 18807
[215:53] Removing low confidence identifications
[215:53] Removing interfering precursors
[215:56] Training neural networks: 55917 targets, 29960 decoys
[215:58] Number of IDs at 0.01 FDR: 19852
[215:59] Calculating protein q-values
[215:59] Number of proteins identified at 1% FDR: 2897 (precursor-level), 2553 (protein-level) (inference performed using proteotypic peptides only)
[215:59] Quantification
[216:01] Quantification information saved to D:\Experiment_2+1_T-B1_1_14506.d.quant.

[216:09] File #10/24
[216:09] Loading run D:\Experiment_2+1_T-B2_1_14507.d
[216:36] 2865545 library precursors are potentially detectable
[216:36] Processing...
[220:49] RT window set to 2.42057
[220:49] Ion mobility window set to 0.0421231
[220:49] Recommended MS1 mass accuracy setting: 15.1285 ppm
[228:47] Removing low confidence identifications
[228:47] Removing interfering precursors
[228:50] Training neural networks: 47388 targets, 27226 decoys
[228:52] Number of IDs at 0.01 FDR: 19378
[237:41] Removing low confidence identifications
[237:41] Removing interfering precursors
[237:44] Training neural networks: 56829 targets, 30500 decoys
[237:47] Number of IDs at 0.01 FDR: 20317
[237:47] Calculating protein q-values
[237:47] Number of proteins identified at 1% FDR: 3019 (precursor-level), 2593 (protein-level) (inference performed using proteotypic peptides only)
[237:48] Quantification
[237:49] Quantification information saved to D:\Experiment_2+1_T-B2_1_14507.d.quant.

[237:58] File #11/24
[237:58] Loading run D:\Experiment_2+2_T-B3_1_14508.d
[238:27] 2865545 library precursors are potentially detectable
[238:27] Processing...
[242:41] RT window set to 2.31646
[242:41] Ion mobility window set to 0.0397428
[242:42] Recommended MS1 mass accuracy setting: 14.2854 ppm
[250:44] Removing low confidence identifications
[250:45] Removing interfering precursors
[250:47] Training neural networks: 44702 targets, 25949 decoys
[250:49] Number of IDs at 0.01 FDR: 18255
[259:45] Removing low confidence identifications
[259:45] Removing interfering precursors
[259:48] Training neural networks: 52101 targets, 28563 decoys
[259:50] Number of IDs at 0.01 FDR: 19409
[259:51] Calculating protein q-values
[259:51] Number of proteins identified at 1% FDR: 3088 (precursor-level), 2720 (protein-level) (inference performed using proteotypic peptides only)
[259:51] Quantification
[259:53] Quantification information saved to D:\Experiment_2+2_T-B3_1_14508.d.quant.

[260:02] File #12/24
[260:02] Loading run D:\Experiment_2+2_T-B4_1_14509.d
[260:33] 2865545 library precursors are potentially detectable
[260:33] Processing...
[265:32] RT window set to 2.28342
[265:32] Ion mobility window set to 0.0396306
[265:32] Recommended MS1 mass accuracy setting: 14.7176 ppm
[273:58] Removing low confidence identifications
[273:58] Removing interfering precursors
[274:00] Training neural networks: 46079 targets, 26629 decoys
[274:03] Number of IDs at 0.01 FDR: 18144
[283:03] Removing low confidence identifications
[283:03] Removing interfering precursors
[283:06] Training neural networks: 56005 targets, 30544 decoys
[283:09] Number of IDs at 0.01 FDR: 18961
[283:09] Calculating protein q-values
[283:10] Number of proteins identified at 1% FDR: 3020 (precursor-level), 2737 (protein-level) (inference performed using proteotypic peptides only)
[283:10] Quantification
[283:12] Quantification information saved to D:\Experiment_2+2_T-B4_1_14509.d.quant.

[283:22] File #13/24
[283:22] Loading run D:\Experiment_2-1_T-A9_1_14502.d
[283:48] 2865545 library precursors are potentially detectable
[283:48] Processing...
[287:56] RT window set to 2.3865
[287:56] Ion mobility window set to 0.0423084
[287:56] Recommended MS1 mass accuracy setting: 14.6251 ppm
[295:30] Removing low confidence identifications
[295:30] Removing interfering precursors
[295:33] Training neural networks: 44435 targets, 25079 decoys
[295:35] Number of IDs at 0.01 FDR: 18406
[304:02] Removing low confidence identifications
[304:03] Removing interfering precursors
[304:05] Training neural networks: 52464 targets, 27881 decoys
[304:07] Number of IDs at 0.01 FDR: 19108
[304:08] Calculating protein q-values
[304:08] Number of proteins identified at 1% FDR: 2786 (precursor-level), 2498 (protein-level) (inference performed using proteotypic peptides only)
[304:08] Quantification
[304:10] Quantification information saved to D:\Experiment_2-1_T-A9_1_14502.d.quant.

[304:19] File #14/24
[304:19] Loading run D:\Experiment_2-1_T-A10_1_14503.d
[304:46] 2865545 library precursors are potentially detectable
[304:46] Processing...
[308:47] RT window set to 2.32819
[308:47] Ion mobility window set to 0.0414816
[308:47] Recommended MS1 mass accuracy setting: 15.4593 ppm
[316:15] Removing low confidence identifications
[316:15] Removing interfering precursors
[316:17] Training neural networks: 44181 targets, 25160 decoys
[316:19] Number of IDs at 0.01 FDR: 18289
[324:36] Removing low confidence identifications
[324:36] Removing interfering precursors
[324:39] Training neural networks: 54476 targets, 29250 decoys
[324:41] Number of IDs at 0.01 FDR: 19211
[324:42] Calculating protein q-values
[324:42] Number of proteins identified at 1% FDR: 2845 (precursor-level), 2534 (protein-level) (inference performed using proteotypic peptides only)
[324:42] Quantification
[324:44] Quantification information saved to D:\Experiment_2-1_T-A10_1_14503.d.quant.

[324:52] File #15/24
[324:52] Loading run D:\Experiment_2-2_T-A11_1_14504.d
[325:22] 2865545 library precursors are potentially detectable
[325:22] Processing...
[329:37] RT window set to 2.29734
[329:37] Ion mobility window set to 0.0391452
[329:37] Recommended MS1 mass accuracy setting: 15.4517 ppm
[337:42] Removing low confidence identifications
[337:42] Removing interfering precursors
[337:44] Training neural networks: 43509 targets, 24900 decoys
[337:46] Number of IDs at 0.01 FDR: 16956
[346:40] Removing low confidence identifications
[346:40] Removing interfering precursors
[346:43] Training neural networks: 52097 targets, 28249 decoys
[346:45] Number of IDs at 0.01 FDR: 18114
[346:46] Calculating protein q-values
[346:46] Number of proteins identified at 1% FDR: 2896 (precursor-level), 2594 (protein-level) (inference performed using proteotypic peptides only)
[346:46] Quantification
[346:48] Quantification information saved to D:\Experiment_2-2_T-A11_1_14504.d.quant.

[346:58] File #16/24
[346:58] Loading run D:\Experiment_2-2_T-A12_1_14505.d
[347:28] 2865545 library precursors are potentially detectable
[347:28] Processing...
[353:15] RT window set to 2.44725
[353:15] Ion mobility window set to 0.0415149
[353:16] Recommended MS1 mass accuracy setting: 15.0652 ppm
[362:35] Removing low confidence identifications
[362:35] Removing interfering precursors
[362:37] Training neural networks: 45004 targets, 25737 decoys
[362:39] Number of IDs at 0.01 FDR: 17456
[372:14] Removing low confidence identifications
[372:14] Removing interfering precursors
[372:16] Training neural networks: 51946 targets, 28024 decoys
[372:19] Number of IDs at 0.01 FDR: 18405
[372:19] Calculating protein q-values
[372:20] Number of proteins identified at 1% FDR: 2912 (precursor-level), 2599 (protein-level) (inference performed using proteotypic peptides only)
[372:20] Quantification
[372:22] Quantification information saved to D:\Experiment_2-2_T-A12_1_14505.d.quant.

[372:32] File #17/24
[372:32] Loading run D:\Experiment_3+1_T-B9_1_14514.d
[372:58] 2865545 library precursors are potentially detectable
[372:58] Processing...
[378:29] RT window set to 2.45396
[378:29] Ion mobility window set to 0.044619
[378:29] Recommended MS1 mass accuracy setting: 14.0472 ppm
[386:47] Removing low confidence identifications
[386:47] Removing interfering precursors
[386:49] Training neural networks: 35399 targets, 19914 decoys
[386:51] Number of IDs at 0.01 FDR: 14307
[395:29] Removing low confidence identifications
[395:30] Removing interfering precursors
[395:32] Training neural networks: 39944 targets, 21306 decoys
[395:34] Number of IDs at 0.01 FDR: 14702
[395:34] Calculating protein q-values
[395:34] Number of proteins identified at 1% FDR: 2328 (precursor-level), 1976 (protein-level) (inference performed using proteotypic peptides only)
[395:34] Quantification
[395:36] Quantification information saved to D:\Experiment_3+1_T-B9_1_14514.d.quant.

[395:45] File #18/24
[395:45] Loading run D:\Experiment_3+1_T-B10_1_14515.d
[396:12] 2865545 library precursors are potentially detectable
[396:13] Processing...
[401:57] RT window set to 2.34775
[401:57] Ion mobility window set to 0.0429978
[401:58] Recommended MS1 mass accuracy setting: 14.7663 ppm
[410:12] Removing low confidence identifications
[410:12] Removing interfering precursors
[410:14] Training neural networks: 34868 targets, 19436 decoys
[410:15] Number of IDs at 0.01 FDR: 13785
[418:48] Removing low confidence identifications
[418:49] Removing interfering precursors
[418:51] Training neural networks: 40621 targets, 21513 decoys
[418:53] Number of IDs at 0.01 FDR: 14298
[418:53] Calculating protein q-values
[418:53] Number of proteins identified at 1% FDR: 2337 (precursor-level), 2033 (protein-level) (inference performed using proteotypic peptides only)
[418:54] Quantification
[418:55] Quantification information saved to D:\Experiment_3+1_T-B10_1_14515.d.quant.

[419:04] File #19/24
[419:04] Loading run D:\Experiment_3+2_T-B11_1_14516.d
[419:32] 2865545 library precursors are potentially detectable
[419:33] Processing...
[425:23] RT window set to 2.41674
[425:23] Ion mobility window set to 0.0433231
[425:23] Recommended MS1 mass accuracy setting: 14.6355 ppm
[434:20] Removing low confidence identifications
[434:20] Removing interfering precursors
[434:22] Training neural networks: 35091 targets, 19440 decoys
[434:24] Number of IDs at 0.01 FDR: 13448
[443:36] Removing low confidence identifications
[443:36] Removing interfering precursors
[443:38] Training neural networks: 41220 targets, 21897 decoys
[443:40] Number of IDs at 0.01 FDR: 14132
[443:41] Calculating protein q-values
[443:41] Number of proteins identified at 1% FDR: 2394 (precursor-level), 2046 (protein-level) (inference performed using proteotypic peptides only)
[443:41] Quantification
[443:43] Quantification information saved to D:\Experiment_3+2_T-B11_1_14516.d.quant.

[443:53] File #20/24
[443:53] Loading run D:\Experiment_3+2_T-B12_1_14517.d
[444:21] 2865545 library precursors are potentially detectable
[444:21] Processing...
[449:24] RT window set to 2.38723
[449:24] Ion mobility window set to 0.0434859
[449:24] Recommended MS1 mass accuracy setting: 14.6485 ppm
[458:02] Removing low confidence identifications
[458:02] Removing interfering precursors
[458:04] Training neural networks: 36912 targets, 20639 decoys
[458:06] Number of IDs at 0.01 FDR: 14643
[467:16] Removing low confidence identifications
[467:17] Removing interfering precursors
[467:19] Training neural networks: 43052 targets, 23100 decoys
[467:21] Number of IDs at 0.01 FDR: 15135
[467:21] Calculating protein q-values
[467:22] Number of proteins identified at 1% FDR: 2414 (precursor-level), 2163 (protein-level) (inference performed using proteotypic peptides only)
[467:22] Quantification
[467:24] Quantification information saved to D:\Experiment_3+2_T-B12_1_14517.d.quant.

[467:33] File #21/24
[467:33] Loading run D:\Experiment_3-1_T-B5_1_14510.d
[468:00] 2865545 library precursors are potentially detectable
[468:00] Processing...
[473:40] RT window set to 2.48635
[473:40] Ion mobility window set to 0.0407078
[473:40] Recommended MS1 mass accuracy setting: 14.4878 ppm
[481:53] Removing low confidence identifications
[481:53] Removing interfering precursors
[481:55] Training neural networks: 36268 targets, 20207 decoys
[481:57] Number of IDs at 0.01 FDR: 14647
[490:29] Removing low confidence identifications
[490:29] Removing interfering precursors
[490:31] Training neural networks: 42880 targets, 22759 decoys
[490:33] Number of IDs at 0.01 FDR: 15274
[490:33] Calculating protein q-values
[490:34] Number of proteins identified at 1% FDR: 2390 (precursor-level), 2093 (protein-level) (inference performed using proteotypic peptides only)
[490:34] Quantification
[490:36] Quantification information saved to D:\Experiment_3-1_T-B5_1_14510.d.quant.

[490:44] File #22/24
[490:44] Loading run D:\Experiment_3-1_T-B6_1_14511.d
[491:10] 2865545 library precursors are potentially detectable
[491:10] Processing...
[496:36] RT window set to 2.40189
[496:36] Ion mobility window set to 0.0426787
[496:37] Recommended MS1 mass accuracy setting: 15.0529 ppm
[504:31] Removing low confidence identifications
[504:31] Removing interfering precursors
[504:33] Training neural networks: 35169 targets, 19670 decoys
[504:35] Number of IDs at 0.01 FDR: 14390
[512:51] Removing low confidence identifications
[512:51] Removing interfering precursors
[512:53] Training neural networks: 40020 targets, 21127 decoys
[512:55] Number of IDs at 0.01 FDR: 14931
[512:56] Calculating protein q-values
[512:56] Number of proteins identified at 1% FDR: 2335 (precursor-level), 1992 (protein-level) (inference performed using proteotypic peptides only)
[512:56] Quantification
[512:58] Quantification information saved to D:\Experiment_3-1_T-B6_1_14511.d.quant.

[513:06] File #23/24
[513:06] Loading run D:\Experiment_3-2_T-B7_1_14512.d
[513:33] 2865545 library precursors are potentially detectable
[513:34] Processing...
[520:14] RT window set to 2.30762
[520:14] Ion mobility window set to 0.0408636
[520:15] Recommended MS1 mass accuracy setting: 14.5797 ppm
[528:27] Removing low confidence identifications
[528:28] Removing interfering precursors
[528:29] Training neural networks: 31186 targets, 17354 decoys
[528:31] Number of IDs at 0.01 FDR: 11983
[536:48] Removing low confidence identifications
[536:48] Removing interfering precursors
[536:50] Training neural networks: 37415 targets, 19742 decoys
[536:52] Number of IDs at 0.01 FDR: 12350
[536:52] Calculating protein q-values
[536:53] Number of proteins identified at 1% FDR: 2089 (precursor-level), 1868 (protein-level) (inference performed using proteotypic peptides only)
[536:53] Quantification
[536:54] Quantification information saved to D:\Experiment_3-2_T-B7_1_14512.d.quant.

[537:03] File #24/24
[537:03] Loading run D:\Experiment_3-2_T-B8_1_14513.d
[537:31] 2865545 library precursors are potentially detectable
[537:31] Processing...
[543:25] RT window set to 2.41482
[543:25] Ion mobility window set to 0.0410586
[543:25] Recommended MS1 mass accuracy setting: 15.3544 ppm
[552:01] Removing low confidence identifications
[552:01] Removing interfering precursors
[552:03] Training neural networks: 32692 targets, 18306 decoys
[552:04] Number of IDs at 0.01 FDR: 12708
[560:58] Removing low confidence identifications
[560:58] Removing interfering precursors
[561:00] Training neural networks: 37723 targets, 20179 decoys
[561:02] Number of IDs at 0.01 FDR: 13108
[561:02] Calculating protein q-values
[561:02] Number of proteins identified at 1% FDR: 2162 (precursor-level), 1958 (protein-level) (inference performed using proteotypic peptides only)
[561:03] Quantification
[561:04] Quantification information saved to D:\Experiment_3-2_T-B8_1_14513.d.quant.

[561:14] Cross-run analysis
[561:14] Reading quantification information: 24 files
[561:17] Quantifying peptides
[561:22] Assembling protein groups
[561:26] Quantifying proteins
[561:26] Calculating q-values for protein and gene groups
[561:27] Calculating global q-values for protein and gene groups
[561:27] Writing report
[561:55] Report saved to D:\MassSpecData\2023\May23\RSharma\report-first-pass.tsv.
[561:55] Saving precursor levels matrix
[561:56] Precursor levels matrix (1% precursor and protein group FDR) saved to D:\MassSpecData\2023\May23\RSharma\report-first-pass.pr_matrix.tsv.
[561:56] Saving protein group levels matrix
[561:56] Protein group levels matrix (1% precursor FDR and protein group FDR) saved to D:\MassSpecData\2023\May23\RSharma\report-first-pass.pg_matrix.tsv.
[561:56] Saving gene group levels matrix
[561:56] Gene groups levels matrix (1% precursor FDR and protein group FDR) saved to D:\MassSpecData\2023\May23\RSharma\report-first-pass.gg_matrix.tsv.
[561:56] Saving unique genes levels matrix
[561:56] Unique genes levels matrix (1% precursor FDR and protein group FDR) saved to D:\MassSpecData\2023\May23\RSharma\report-first-pass.unique_genes_matrix.tsv.
[561:56] Stats report saved to D:\MassSpecData\2023\May23\RSharma\report-first-pass.stats.tsv
[561:56] Generating spectral library:
[561:56] 38713 precursors passing the FDR threshold are to be extracted
[561:56] Loading run D:\Experiment_1+1_T-A5_1_14498.d
[562:25] 2865545 library precursors are potentially detectable
[562:26] 970 spectra added to the library
[562:30] Loading run D:\Experiment_1+1_T-A6_1_14499.d
[563:00] 2865545 library precursors are potentially detectable
[563:00] 780 spectra added to the library
[563:06] Loading run D:\Experiment_1+2_T-A7_1_14500.d
[563:32] 2865545 library precursors are potentially detectable
[563:33] 1920 spectra added to the library
[563:38] Loading run D:\Experiment_1+2_T-A8_1_14501.d
[564:06] 2865545 library precursors are potentially detectable
[564:07] 3011 spectra added to the library
[564:12] Loading run D:\Experiment_1-1_T-A1_1_14494.d
[564:39] 2865545 library precursors are potentially detectable
[564:41] 5083 spectra added to the library
[564:46] Loading run D:\Experiment_1-1_T-A2_1_14495.d
[565:15] 2865545 library precursors are potentially detectable
[565:16] 1568 spectra added to the library
[565:21] Loading run D:\Experiment_1-2_T-A3_1_14496.d
[565:48] 2865545 library precursors are potentially detectable
[565:49] 1789 spectra added to the library
[565:54] Loading run D:\Experiment_1-2_T-A4_1_14497.d
[566:22] 2865545 library precursors are potentially detectable
[566:22] 414 spectra added to the library
[566:27] Loading run D:\Experiment_2+1_T-B1_1_14506.d
[566:54] 2865545 library precursors are potentially detectable
[566:55] 1204 spectra added to the library
[567:00] Loading run D:\Experiment_2+1_T-B2_1_14507.d
[567:27] 2865545 library precursors are potentially detectable
[567:28] 3230 spectra added to the library
[567:33] Loading run D:\Experiment_2+2_T-B3_1_14508.d
[568:02] 2865545 library precursors are potentially detectable
[568:02] 787 spectra added to the library
[568:08] Loading run D:\Experiment_2+2_T-B4_1_14509.d
[568:37] 2865545 library precursors are potentially detectable
[568:38] 1259 spectra added to the library
[568:44] Loading run D:\Experiment_2-1_T-A9_1_14502.d
[569:10] 2865545 library precursors are potentially detectable
[569:10] 288 spectra added to the library
[569:16] Loading run D:\Experiment_2-1_T-A10_1_14503.d
[569:42] 2865545 library precursors are potentially detectable
[569:42] 628 spectra added to the library
[569:47] Loading run D:\Experiment_2-2_T-A11_1_14504.d
[570:17] 2865545 library precursors are potentially detectable
[570:17] 541 spectra added to the library
[570:23] Loading run D:\Experiment_2-2_T-A12_1_14505.d
[570:53] 2865545 library precursors are potentially detectable
[570:54] 869 spectra added to the library
[571:00] Loading run D:\Experiment_3+1_T-B9_1_14514.d
[571:26] 2865545 library precursors are potentially detectable
[571:26] 302 spectra added to the library
[571:31] Loading run D:\Experiment_3+1_T-B10_1_14515.d
[571:58] 2865545 library precursors are potentially detectable
[571:58] 728 spectra added to the library
[572:03] Loading run D:\Experiment_3+2_T-B11_1_14516.d
[572:31] 2865545 library precursors are potentially detectable
[572:32] 238 spectra added to the library
[572:38] Loading run D:\Experiment_3+2_T-B12_1_14517.d
[573:05] 2865545 library precursors are potentially detectable
[573:06] 563 spectra added to the library
[573:11] Loading run D:\Experiment_3-1_T-B5_1_14510.d
[573:37] 2865545 library precursors are potentially detectable
[573:37] 168 spectra added to the library
[573:42] Loading run D:\Experiment_3-1_T-B6_1_14511.d
[574:08] 2865545 library precursors are potentially detectable
[574:08] 750 spectra added to the library
[574:13] Loading run D:\Experiment_3-2_T-B7_1_14512.d
[574:40] 2865545 library precursors are potentially detectable
[574:40] 323 spectra added to the library
[574:46] Loading run D:\Experiment_3-2_T-B8_1_14513.d
[575:13] 2865545 library precursors are potentially detectable
[575:13] 893 spectra added to the library
[575:18] Saving spectral library to D:\MassSpecData\2023\May23\RSharma\lib.tsv
[575:24] 38713 precursors saved
[575:24] Loading the generated library and saving it in the .speclib format
[575:24] Loading spectral library D:\MassSpecData\2023\May23\RSharma\lib.tsv
[575:26] Spectral library loaded: 11599 protein isoforms, 7643 protein groups and 38713 precursors in 34515 elution groups.
[575:26] Loading protein annotations from FASTA D:\FastaDatabases\KWynne\Human\uniprot-download_true_format_fasta_includeIsoform_true_query__28Huma-2023.01.27-16.04.15.82.fasta
[575:27] Gene names missing for some isoforms
[575:27] Library contains 5396 proteins, and 5383 genes
[575:27] Saving the library to D:\MassSpecData\2023\May23\RSharma\lib.tsv.speclib

[575:31] Second pass: using the newly created spectral library to reanalyse the data
[575:31] File #1/24
[575:31] Loading run D:\Experiment_1+1_T-A5_1_14498.d
[575:59] 38713 library precursors are potentially detectable
[575:59] Processing...
[576:04] RT window set to 0.909976
[576:04] Ion mobility window set to 0.0123047
[576:04] Recommended MS1 mass accuracy setting: 16.9868 ppm
[576:08] Removing low confidence identifications
[576:08] Removing interfering precursors
[576:09] Training neural networks: 37662 targets, 30183 decoys
[576:11] Number of IDs at 0.01 FDR: 32479
[576:16] Removing low confidence identifications
[576:16] Removing interfering precursors
[576:17] Training neural networks: 37733 targets, 30202 decoys
[576:19] Number of IDs at 0.01 FDR: 32412
[576:20] Calculating protein q-values
[576:20] Number of proteins identified at 1% FDR: 4225 (precursor-level), 3553 (protein-level) (inference performed using proteotypic peptides only)
[576:20] Quantification

[576:28] File #2/24
[576:28] Loading run D:\Experiment_1+1_T-A6_1_14499.d
[576:57] 38713 library precursors are potentially detectable
[576:57] Processing...
[577:01] RT window set to 0.912598
[577:01] Ion mobility window set to 0.0120341
[577:01] Recommended MS1 mass accuracy setting: 16.1042 ppm
[577:05] Removing low confidence identifications
[577:05] Removing interfering precursors
[577:06] Training neural networks: 37719 targets, 30140 decoys
[577:08] Number of IDs at 0.01 FDR: 32325
[577:14] Removing low confidence identifications
[577:14] Removing interfering precursors
[577:15] Training neural networks: 37772 targets, 30168 decoys
[577:17] Number of IDs at 0.01 FDR: 32281
[577:17] Calculating protein q-values
[577:17] Number of proteins identified at 1% FDR: 4209 (precursor-level), 3518 (protein-level) (inference performed using proteotypic peptides only)
[577:17] Quantification

[577:26] File #3/24
[577:26] Loading run D:\Experiment_1+2_T-A7_1_14500.d
[577:52] 38713 library precursors are potentially detectable
[577:52] Processing...
[577:56] RT window set to 0.90114
[577:56] Ion mobility window set to 0.0118125
[577:56] Recommended MS1 mass accuracy setting: 15.8281 ppm
[577:59] Removing low confidence identifications
[577:59] Removing interfering precursors
[578:01] Training neural networks: 37303 targets, 27912 decoys
[578:03] Number of IDs at 0.01 FDR: 34247
[578:08] Removing low confidence identifications
[578:08] Removing interfering precursors
[578:09] Training neural networks: 37347 targets, 27899 decoys
[578:11] Number of IDs at 0.01 FDR: 34361
[578:11] Calculating protein q-values
[578:11] Number of proteins identified at 1% FDR: 4296 (precursor-level), 3553 (protein-level) (inference performed using proteotypic peptides only)
[578:11] Quantification

[578:19] File #4/24
[578:19] Loading run D:\Experiment_1+2_T-A8_1_14501.d
[578:46] 38713 library precursors are potentially detectable
[578:46] Processing...
[578:50] RT window set to 0.906232
[578:50] Ion mobility window set to 0.0118359
[578:50] Recommended MS1 mass accuracy setting: 16.1728 ppm
[578:54] Removing low confidence identifications
[578:54] Removing interfering precursors
[578:55] Training neural networks: 37300 targets, 27887 decoys
[578:57] Number of IDs at 0.01 FDR: 34087
[579:02] Removing low confidence identifications
[579:02] Removing interfering precursors
[579:03] Training neural networks: 37350 targets, 27882 decoys
[579:05] Number of IDs at 0.01 FDR: 34201
[579:06] Calculating protein q-values
[579:06] Number of proteins identified at 1% FDR: 4302 (precursor-level), 3564 (protein-level) (inference performed using proteotypic peptides only)
[579:06] Quantification

[579:14] File #5/24
[579:14] Loading run D:\Experiment_1-1_T-A1_1_14494.d
[579:42] 38713 library precursors are potentially detectable
[579:42] Processing...
[579:46] RT window set to 0.920786
[579:46] Ion mobility window set to 0.0118441
[579:46] Recommended MS1 mass accuracy setting: 16.3091 ppm
[579:50] Removing low confidence identifications
[579:50] Removing interfering precursors
[579:52] Training neural networks: 37832 targets, 30904 decoys
[579:54] Number of IDs at 0.01 FDR: 33106
[579:59] Removing low confidence identifications
[579:59] Removing interfering precursors
[580:00] Training neural networks: 37879 targets, 30928 decoys
[580:02] Number of IDs at 0.01 FDR: 33084
[580:03] Calculating protein q-values
[580:03] Number of proteins identified at 1% FDR: 4235 (precursor-level), 3479 (protein-level) (inference performed using proteotypic peptides only)
[580:03] Quantification

[580:12] File #6/24
[580:12] Loading run D:\Experiment_1-1_T-A2_1_14495.d
[580:39] 38713 library precursors are potentially detectable
[580:40] Processing...
[580:44] RT window set to 0.916531
[580:44] Ion mobility window set to 0.0120573
[580:44] Recommended MS1 mass accuracy setting: 16.5401 ppm
[580:48] Removing low confidence identifications
[580:48] Removing interfering precursors
[580:49] Training neural networks: 37695 targets, 29559 decoys
[580:51] Number of IDs at 0.01 FDR: 33606
[580:56] Removing low confidence identifications
[580:56] Removing interfering precursors
[580:57] Training neural networks: 37726 targets, 29602 decoys
[580:59] Number of IDs at 0.01 FDR: 33650
[581:00] Calculating protein q-values
[581:00] Number of proteins identified at 1% FDR: 4287 (precursor-level), 3525 (protein-level) (inference performed using proteotypic peptides only)
[581:00] Quantification

[581:08] File #7/24
[581:08] Loading run D:\Experiment_1-2_T-A3_1_14496.d
[581:35] 38713 library precursors are potentially detectable
[581:35] Processing...
[581:39] RT window set to 0.916665
[581:39] Ion mobility window set to 0.011875
[581:39] Recommended MS1 mass accuracy setting: 17.0313 ppm
[581:43] Removing low confidence identifications
[581:43] Removing interfering precursors
[581:45] Training neural networks: 37368 targets, 28254 decoys
[581:46] Number of IDs at 0.01 FDR: 34226
[581:51] Removing low confidence identifications
[581:51] Removing interfering precursors
[581:53] Training neural networks: 37394 targets, 28303 decoys
[581:54] Number of IDs at 0.01 FDR: 34271
[581:55] Calculating protein q-values
[581:55] Number of proteins identified at 1% FDR: 4267 (precursor-level), 3587 (protein-level) (inference performed using proteotypic peptides only)
[581:55] Quantification

[582:03] File #8/24
[582:03] Loading run D:\Experiment_1-2_T-A4_1_14497.d
[582:31] 38713 library precursors are potentially detectable
[582:31] Processing...
[582:35] RT window set to 0.918744
[582:35] Ion mobility window set to 0.0121953
[582:35] Recommended MS1 mass accuracy setting: 15.8071 ppm
[582:39] Removing low confidence identifications
[582:39] Removing interfering precursors
[582:40] Training neural networks: 37396 targets, 28804 decoys
[582:42] Number of IDs at 0.01 FDR: 33155
[582:47] Removing low confidence identifications
[582:47] Removing interfering precursors
[582:48] Training neural networks: 37447 targets, 28787 decoys
[582:50] Number of IDs at 0.01 FDR: 33225
[582:51] Calculating protein q-values
[582:51] Number of proteins identified at 1% FDR: 4246 (precursor-level), 3541 (protein-level) (inference performed using proteotypic peptides only)
[582:51] Quantification

[582:59] File #9/24
[582:59] Loading run D:\Experiment_2+1_T-B1_1_14506.d
[583:25] 38713 library precursors are potentially detectable
[583:25] Processing...
[583:30] RT window set to 0.895274
[583:30] Ion mobility window set to 0.0119093
[583:30] Recommended MS1 mass accuracy setting: 15.5486 ppm
[583:33] Removing low confidence identifications
[583:33] Removing interfering precursors
[583:34] Training neural networks: 36060 targets, 26825 decoys
[583:36] Number of IDs at 0.01 FDR: 29715
[583:41] Removing low confidence identifications
[583:41] Removing interfering precursors
[583:42] Training neural networks: 36151 targets, 26803 decoys
[583:43] Number of IDs at 0.01 FDR: 29633
[583:44] Calculating protein q-values
[583:44] Number of proteins identified at 1% FDR: 3951 (precursor-level), 3183 (protein-level) (inference performed using proteotypic peptides only)
[583:44] Quantification

[583:52] File #10/24
[583:52] Loading run D:\Experiment_2+1_T-B2_1_14507.d
[584:18] 38713 library precursors are potentially detectable
[584:18] Processing...
[584:22] RT window set to 0.90204
[584:22] Ion mobility window set to 0.0117448
[584:22] Recommended MS1 mass accuracy setting: 16.0276 ppm
[584:26] Removing low confidence identifications
[584:26] Removing interfering precursors
[584:27] Training neural networks: 36425 targets, 27799 decoys
[584:29] Number of IDs at 0.01 FDR: 29736
[584:34] Removing low confidence identifications
[584:34] Removing interfering precursors
[584:35] Training neural networks: 36529 targets, 27804 decoys
[584:37] Number of IDs at 0.01 FDR: 29708
[584:37] Calculating protein q-values
[584:37] Number of proteins identified at 1% FDR: 3956 (precursor-level), 3240 (protein-level) (inference performed using proteotypic peptides only)
[584:37] Quantification

[584:45] File #11/24
[584:45] Loading run D:\Experiment_2+2_T-B3_1_14508.d
[585:13] 38713 library precursors are potentially detectable
[585:13] Processing...
[585:17] RT window set to 0.911479
[585:17] Ion mobility window set to 0.0122656
[585:17] Recommended MS1 mass accuracy setting: 15.4615 ppm
[585:21] Removing low confidence identifications
[585:21] Removing interfering precursors
[585:22] Training neural networks: 37013 targets, 29724 decoys
[585:24] Number of IDs at 0.01 FDR: 28488
[585:29] Removing low confidence identifications
[585:29] Removing interfering precursors
[585:31] Training neural networks: 37078 targets, 29779 decoys
[585:32] Number of IDs at 0.01 FDR: 28461
[585:33] Calculating protein q-values
[585:33] Number of proteins identified at 1% FDR: 3919 (precursor-level), 3225 (protein-level) (inference performed using proteotypic peptides only)
[585:33] Quantification

[585:41] File #12/24
[585:41] Loading run D:\Experiment_2+2_T-B4_1_14509.d
[586:10] 38713 library precursors are potentially detectable
[586:10] Processing...
[586:15] RT window set to 0.912307
[586:15] Ion mobility window set to 0.012362
[586:15] Recommended MS1 mass accuracy setting: 15.2686 ppm
[586:19] Removing low confidence identifications
[586:19] Removing interfering precursors
[586:20] Training neural networks: 37116 targets, 30228 decoys
[586:22] Number of IDs at 0.01 FDR: 28042
[586:27] Removing low confidence identifications
[586:27] Removing interfering precursors
[586:28] Training neural networks: 37196 targets, 30236 decoys
[586:30] Number of IDs at 0.01 FDR: 28060
[586:30] Calculating protein q-values
[586:30] Number of proteins identified at 1% FDR: 3919 (precursor-level), 3204 (protein-level) (inference performed using proteotypic peptides only)
[586:30] Quantification

[586:39] File #13/24
[586:39] Loading run D:\Experiment_2-1_T-A9_1_14502.d
[587:04] 38713 library precursors are potentially detectable
[587:04] Processing...
[587:08] RT window set to 0.903435
[587:08] Ion mobility window set to 0.0117682
[587:08] Recommended MS1 mass accuracy setting: 15.7095 ppm
[587:12] Removing low confidence identifications
[587:12] Removing interfering precursors
[587:13] Training neural networks: 35945 targets, 26860 decoys
[587:15] Number of IDs at 0.01 FDR: 29255
[587:19] Removing low confidence identifications
[587:19] Removing interfering precursors
[587:20] Training neural networks: 36027 targets, 26939 decoys
[587:22] Number of IDs at 0.01 FDR: 29120
[587:23] Calculating protein q-values
[587:23] Number of proteins identified at 1% FDR: 3866 (precursor-level), 3194 (protein-level) (inference performed using proteotypic peptides only)
[587:23] Quantification

[587:31] File #14/24
[587:31] Loading run D:\Experiment_2-1_T-A10_1_14503.d
[587:56] 38713 library precursors are potentially detectable
[587:56] Processing...
[588:00] RT window set to 0.895307
[588:00] Ion mobility window set to 0.0117786
[588:00] Recommended MS1 mass accuracy setting: 16.0109 ppm
[588:04] Removing low confidence identifications
[588:04] Removing interfering precursors
[588:05] Training neural networks: 36076 targets, 27229 decoys
[588:07] Number of IDs at 0.01 FDR: 28965
[588:11] Removing low confidence identifications
[588:11] Removing interfering precursors
[588:13] Training neural networks: 36164 targets, 27213 decoys
[588:14] Number of IDs at 0.01 FDR: 29022
[588:15] Calculating protein q-values
[588:15] Number of proteins identified at 1% FDR: 3895 (precursor-level), 3178 (protein-level) (inference performed using proteotypic peptides only)
[588:15] Quantification

[588:23] File #15/24
[588:23] Loading run D:\Experiment_2-2_T-A11_1_14504.d
[588:52] 38713 library precursors are potentially detectable
[588:52] Processing...
[588:56] RT window set to 0.912741
[588:56] Ion mobility window set to 0.0121927
[588:56] Recommended MS1 mass accuracy setting: 15.5146 ppm
[589:00] Removing low confidence identifications
[589:00] Removing interfering precursors
[589:01] Training neural networks: 36977 targets, 30196 decoys
[589:03] Number of IDs at 0.01 FDR: 26900
[589:09] Removing low confidence identifications
[589:09] Removing interfering precursors
[589:10] Training neural networks: 37038 targets, 30222 decoys
[589:12] Number of IDs at 0.01 FDR: 26843
[589:12] Calculating protein q-values
[589:12] Number of proteins identified at 1% FDR: 3803 (precursor-level), 3129 (protein-level) (inference performed using proteotypic peptides only)
[589:12] Quantification

[589:21] File #16/24
[589:21] Loading run D:\Experiment_2-2_T-A12_1_14505.d
[589:50] 38713 library precursors are potentially detectable
[589:50] Processing...
[589:55] RT window set to 0.913988
[589:55] Ion mobility window set to 0.0122422
[589:55] Recommended MS1 mass accuracy setting: 16.0555 ppm
[589:59] Removing low confidence identifications
[589:59] Removing interfering precursors
[590:00] Training neural networks: 37101 targets, 30572 decoys
[590:02] Number of IDs at 0.01 FDR: 27434
[590:07] Removing low confidence identifications
[590:07] Removing interfering precursors
[590:08] Training neural networks: 37163 targets, 30591 decoys
[590:10] Number of IDs at 0.01 FDR: 27209
[590:10] Calculating protein q-values
[590:10] Number of proteins identified at 1% FDR: 3819 (precursor-level), 3143 (protein-level) (inference performed using proteotypic peptides only)
[590:10] Quantification

[590:20] File #17/24
[590:20] Loading run D:\Experiment_3+1_T-B9_1_14514.d
[590:45] 38713 library precursors are potentially detectable
[590:45] Processing...
[590:49] RT window set to 0.90917
[590:49] Ion mobility window set to 0.0128498
[590:49] Recommended MS1 mass accuracy setting: 16.4965 ppm
[590:52] Removing low confidence identifications
[590:52] Removing interfering precursors
[590:53] Training neural networks: 34221 targets, 27002 decoys
[590:55] Number of IDs at 0.01 FDR: 21477
[591:00] Removing low confidence identifications
[591:00] Removing interfering precursors
[591:01] Training neural networks: 34263 targets, 27072 decoys
[591:02] Number of IDs at 0.01 FDR: 21513
[591:03] Calculating protein q-values
[591:03] Number of proteins identified at 1% FDR: 3189 (precursor-level), 2555 (protein-level) (inference performed using proteotypic peptides only)
[591:03] Quantification

[591:11] File #18/24
[591:11] Loading run D:\Experiment_3+1_T-B10_1_14515.d
[591:37] 38713 library precursors are potentially detectable
[591:37] Processing...
[591:41] RT window set to 0.910181
[591:41] Ion mobility window set to 0.0126764
[591:41] Recommended MS1 mass accuracy setting: 16.1301 ppm
[591:44] Removing low confidence identifications
[591:44] Removing interfering precursors
[591:46] Training neural networks: 34594 targets, 27966 decoys
[591:47] Number of IDs at 0.01 FDR: 21160
[591:52] Removing low confidence identifications
[591:52] Removing interfering precursors
[591:53] Training neural networks: 34682 targets, 28040 decoys
[591:55] Number of IDs at 0.01 FDR: 21158
[591:55] Calculating protein q-values
[591:55] Number of proteins identified at 1% FDR: 3217 (precursor-level), 2551 (protein-level) (inference performed using proteotypic peptides only)
[591:55] Quantification

[592:03] File #19/24
[592:03] Loading run D:\Experiment_3+2_T-B11_1_14516.d
[592:30] 38713 library precursors are potentially detectable
[592:30] Processing...
[592:34] RT window set to 0.913005
[592:34] Ion mobility window set to 0.0135737
[592:34] Recommended MS1 mass accuracy setting: 15.5893 ppm
[592:38] Removing low confidence identifications
[592:38] Removing interfering precursors
[592:39] Training neural networks: 35352 targets, 29851 decoys
[592:41] Number of IDs at 0.01 FDR: 20621
[592:46] Removing low confidence identifications
[592:46] Removing interfering precursors
[592:47] Training neural networks: 35464 targets, 29818 decoys
[592:49] Number of IDs at 0.01 FDR: 20764
[592:49] Calculating protein q-values
[592:49] Number of proteins identified at 1% FDR: 3209 (precursor-level), 2569 (protein-level) (inference performed using proteotypic peptides only)
[592:49] Quantification

[592:58] File #20/24
[592:58] Loading run D:\Experiment_3+2_T-B12_1_14517.d
[593:24] 38713 library precursors are potentially detectable
[593:24] Processing...
[593:28] RT window set to 0.9111
[593:28] Ion mobility window set to 0.0132497
[593:28] Recommended MS1 mass accuracy setting: 16.4797 ppm
[593:32] Removing low confidence identifications
[593:32] Removing interfering precursors
[593:33] Training neural networks: 35464 targets, 29622 decoys
[593:35] Number of IDs at 0.01 FDR: 21781
[593:40] Removing low confidence identifications
[593:40] Removing interfering precursors
[593:41] Training neural networks: 35614 targets, 29666 decoys
[593:42] Number of IDs at 0.01 FDR: 21958
[593:43] Calculating protein q-values
[593:43] Number of proteins identified at 1% FDR: 3293 (precursor-level), 2562 (protein-level) (inference performed using proteotypic peptides only)
[593:43] Quantification

[593:52] File #21/24
[593:52] Loading run D:\Experiment_3-1_T-B5_1_14510.d
[594:17] 38713 library precursors are potentially detectable
[594:17] Processing...
[594:21] RT window set to 0.894079
[594:21] Ion mobility window set to 0.012099
[594:21] Recommended MS1 mass accuracy setting: 15.9377 ppm
[594:25] Removing low confidence identifications
[594:25] Removing interfering precursors
[594:25] Training neural networks: 33839 targets, 26035 decoys
[594:27] Number of IDs at 0.01 FDR: 22127
[594:31] Removing low confidence identifications
[594:31] Removing interfering precursors
[594:32] Training neural networks: 33918 targets, 26093 decoys
[594:34] Number of IDs at 0.01 FDR: 22135
[594:34] Calculating protein q-values
[594:34] Number of proteins identified at 1% FDR: 3260 (precursor-level), 2635 (protein-level) (inference performed using proteotypic peptides only)
[594:34] Quantification

[594:42] File #22/24
[594:42] Loading run D:\Experiment_3-1_T-B6_1_14511.d
[595:07] 38713 library precursors are potentially detectable
[595:07] Processing...
[595:11] RT window set to 0.904076
[595:11] Ion mobility window set to 0.0125503
[595:11] Recommended MS1 mass accuracy setting: 15.5631 ppm
[595:14] Removing low confidence identifications
[595:14] Removing interfering precursors
[595:15] Training neural networks: 33827 targets, 26111 decoys
[595:17] Number of IDs at 0.01 FDR: 21705
[595:21] Removing low confidence identifications
[595:21] Removing interfering precursors
[595:22] Training neural networks: 33917 targets, 26112 decoys
[595:24] Number of IDs at 0.01 FDR: 21674
[595:24] Calculating protein q-values
[595:24] Number of proteins identified at 1% FDR: 3161 (precursor-level), 2533 (protein-level) (inference performed using proteotypic peptides only)
[595:24] Quantification

[595:32] File #23/24
[595:32] Loading run D:\Experiment_3-2_T-B7_1_14512.d
[595:57] 38713 library precursors are potentially detectable
[595:57] Processing...
[596:01] RT window set to 0.901333
[596:01] Ion mobility window set to 0.011862
[596:01] Recommended MS1 mass accuracy setting: 16.1082 ppm
[596:05] Removing low confidence identifications
[596:05] Removing interfering precursors
[596:06] Training neural networks: 33955 targets, 27177 decoys
[596:07] Number of IDs at 0.01 FDR: 19620
[596:12] Removing low confidence identifications
[596:12] Removing interfering precursors
[596:13] Training neural networks: 34057 targets, 27212 decoys
[596:14] Number of IDs at 0.01 FDR: 19681
[596:15] Calculating protein q-values
[596:15] Number of proteins identified at 1% FDR: 3012 (precursor-level), 2428 (protein-level) (inference performed using proteotypic peptides only)
[596:15] Quantification

[596:23] File #24/24
[596:23] Loading run D:\Experiment_3-2_T-B8_1_14513.d
[596:49] 38713 library precursors are potentially detectable
[596:49] Processing...
[596:53] RT window set to 0.887948
[596:53] Ion mobility window set to 0.0125737
[596:53] Recommended MS1 mass accuracy setting: 15.8524 ppm
[596:57] Removing low confidence identifications
[596:57] Removing interfering precursors
[596:58] Training neural networks: 34618 targets, 28481 decoys
[597:00] Number of IDs at 0.01 FDR: 20140
[597:04] Removing low confidence identifications
[597:04] Removing interfering precursors
[597:05] Training neural networks: 34716 targets, 28570 decoys
[597:07] Number of IDs at 0.01 FDR: 20139
[597:07] Calculating protein q-values
[597:07] Number of proteins identified at 1% FDR: 3030 (precursor-level), 2426 (protein-level) (inference performed using proteotypic peptides only)
[597:07] Quantification

[597:16] Cross-run analysis
[597:16] Reading quantification information: 24 files
[597:18] Quantifying peptides
[597:25] Quantifying proteins
[597:26] Calculating q-values for protein and gene groups
[597:27] Calculating global q-values for protein and gene groups
[597:27] Writing report
[598:05] Report saved to D:\MassSpecData\2023\May23\RSharma\report.tsv.
[598:05] Saving precursor levels matrix
[598:06] Precursor levels matrix (1% precursor and protein group FDR) saved to D:\MassSpecData\2023\May23\RSharma\report.pr_matrix.tsv.
[598:06] Saving protein group levels matrix
[598:06] Protein group levels matrix (1% precursor FDR and protein group FDR) saved to D:\MassSpecData\2023\May23\RSharma\report.pg_matrix.tsv.
[598:06] Saving gene group levels matrix
[598:06] Gene groups levels matrix (1% precursor FDR and protein group FDR) saved to D:\MassSpecData\2023\May23\RSharma\report.gg_matrix.tsv.
[598:06] Saving unique genes levels matrix
[598:06] Unique genes levels matrix (1% precursor FDR and protein group FDR) saved to D:\MassSpecData\2023\May23\RSharma\report.unique_genes_matrix.tsv.
[598:06] Stats report saved to D:\MassSpecData\2023\May23\RSharma\report.stats.tsv

Finished

