
DIA-NN 2.1.0 Academia  (Data-Independent Acquisition by Neural Networks)
Compiled on Mar 25 2025 10:59:54
Current date and time: Tue Oct 28 10:23:02 2025
CPU: GenuineIntel Intel(R) Xeon(R) Gold 5220R CPU @ 2.20GHz
SIMD instructions: AVX AVX2 AVX512CD AVX512F FMA SSE4.1 SSE4.2 
Logical CPU cores: 48
diann.exe --f D:\LFQ\8600_Nano_Data\LFQ_ZenoTOF8600_ZenoSWATH_85VW_15min_Nano_50ng_Condition_B_REP3.wiff  --f D:\LFQ\8600_Nano_Data\LFQ_ZenoTOF8600_ZenoSWATH_85VW_15min_Nano_50ng_Condition_B_REP2.wiff  --f D:\LFQ\8600_Nano_Data\LFQ_ZenoTOF8600_ZenoSWATH_85VW_15min_Nano_50ng_Condition_B_REP1.wiff  --f D:\LFQ\8600_Nano_Data\LFQ_ZenoTOF8600_ZenoSWATH_85VW_15min_Nano_50ng_Condition_A_REP3.wiff  --f D:\LFQ\8600_Nano_Data\LFQ_ZenoTOF8600_ZenoSWATH_85VW_15min_Nano_50ng_Condition_A_REP2.wiff  --f D:\LFQ\8600_Nano_Data\LFQ_ZenoTOF8600_ZenoSWATH_85VW_15min_Nano_50ng_Condition_A_REP1.wiff  --lib Q:\ACTIVE\2024\LFQbenchmark_2ndgeneration\DATA_ANALYSIS\DIANN\250418_predicted_HYE_Universalcontaminants.predicted.speclib --threads 24 --verbose 1 --out Q:\ACTIVE\2024\LFQbenchmark_2ndgeneration\DATA_ANALYSIS\DIANN\ZenoTOF8600\251028_DIANNv210_Proteobench_ZenoSWATH_AB_3replicates_report.parquet --qvalue 0.01 --matrices --out-lib Q:\ACTIVE\2024\LFQbenchmark_2ndgeneration\DATA_ANALYSIS\DIANN\ZenoTOF8600\251028_DIANNv210_Proteobench_ZenoSWATH_AB_3replicates_report-lib.parquet --gen-spec-lib --unimod4 --var-mods 1 --var-mod UniMod:35,15.994915,M --var-mod UniMod:1,42.010565,*n --peptidoforms --reanalyse --rt-profiling --no-norm 

Thread number set to 24
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
Cysteine carbamidomethylation enabled as a fixed modification
Maximum number of variable modifications set to 1
Modification UniMod:35 with mass delta 15.9949 at M will be considered as variable
Modification UniMod:1 with mass delta 42.0106 at *n will be considered as variable
Peptidoform scoring enabled
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
Normalisation disabled
DIA-NN will automatically optimise the mass accuracy for the first run of the experiment, use this mode for preliminary analyses only
WARNING: protein inference is enabled but no FASTA provided - is this intended?
The following variable modifications will be localised: UniMod:35 UniMod:1 

6 files will be processed
[0:00] Loading spectral library Q:\ACTIVE\2024\LFQbenchmark_2ndgeneration\DATA_ANALYSIS\DIANN\250418_predicted_HYE_Universalcontaminants.predicted.speclib
[0:55] Library annotated with sequence database(s): Q:\ACTIVE\2024\LFQbenchmark_2ndgeneration\FASTA\uniprotkb_proteome_HYE_UniversalContaminants.fasta
[0:55] Gene names missing for some isoforms
[0:55] Library contains 31265 proteins, and 30133 genes
[0:58] Spectral library loaded: 31416 protein isoforms, 54863 protein groups and 12423235 precursors in 5742916 elution groups.
[1:13] Initialising library

First pass: generating a spectral library from DIA data

[2:22] File #1/6
[2:22] Loading run D:\LFQ\8600_Nano_Data\LFQ_ZenoTOF8600_ZenoSWATH_85VW_15min_Nano_50ng_Condition_B_REP3.wiff
[5:18] Pre-processing...
[5:32] 1747 MS1 and 148495 MS2 scans in 1747 (inferred) and 1747 (encoded) cycles, 7899997 precursors in range
[5:46] Calibrating with mass accuracies 25 (MS1), 25 (MS2)
[7:36] RT window set to 1.20262
[7:36] Peak width: 2.376
[7:36] Scan window radius set to 5
[7:36] Recommended MS1 mass accuracy setting: 29 ppm
[11:08] Optimised mass accuracy: 17 ppm
[12:30] Searching decoys
[16:55] Main search
[25:37] Removing low confidence identifications
[26:18] Removing interfering precursors
[26:45] Training neural networks on 167240 target and 126355 decoy PSMs
[29:18] Training neural networks on 167240 target and 122859 decoy PSMs
[31:29] Number of IDs at 0.01 FDR: 84224
[31:31] Precursors at 1% peptidoform FDR: 80804
[31:34] Calculating protein q-values
[31:36] Number of genes identified at 1% FDR: 9280 (precursor-level), 8268 (protein-level) (inference performed using proteotypic peptides only)
[31:36] Quantification
[31:38] Precursors with scored PTMs at 1% FDR: 1870 out of 2158 considered
[31:38] Precursors with all scored PTM sites unoccupied at 1% FDR: 78934
[31:38] Precursors with PTMs localised (when required) with > 90% confidence: 993 out of 1870
[31:40] Quantification information saved to D:\LFQ\8600_Nano_Data\LFQ_ZenoTOF8600_ZenoSWATH_85VW_15min_Nano_50ng_Condition_B_REP3.wiff.quant

[31:41] File #2/6
[31:41] Loading run D:\LFQ\8600_Nano_Data\LFQ_ZenoTOF8600_ZenoSWATH_85VW_15min_Nano_50ng_Condition_B_REP2.wiff
[35:00] Pre-processing...
[35:13] 1747 MS1 and 148495 MS2 scans in 1747 (inferred) and 1747 (encoded) cycles, 7899997 precursors in range
[35:26] Calibrating with mass accuracies 29 (MS1), 25 (MS2)
[37:02] RT window set to 1.22369
[37:02] Recommended MS1 mass accuracy setting: 31 ppm
[39:44] Searching decoys
[45:20] Main search
[57:14] Removing low confidence identifications
[58:31] Removing interfering precursors
[59:28] Training neural networks on 160798 target and 118866 decoy PSMs
[63:27] Training neural networks on 160798 target and 114872 decoy PSMs
[66:34] Number of IDs at 0.01 FDR: 84418
[66:39] Precursors at 1% peptidoform FDR: 80126
[66:45] Calculating protein q-values
[66:48] Number of genes identified at 1% FDR: 9329 (precursor-level), 8271 (protein-level) (inference performed using proteotypic peptides only)
[66:49] Quantification
[66:51] Precursors with scored PTMs at 1% FDR: 1371 out of 1859 considered
[66:51] Precursors with all scored PTM sites unoccupied at 1% FDR: 78755
[66:51] Precursors with PTMs localised (when required) with > 90% confidence: 569 out of 1371
[66:56] Quantification information saved to D:\LFQ\8600_Nano_Data\LFQ_ZenoTOF8600_ZenoSWATH_85VW_15min_Nano_50ng_Condition_B_REP2.wiff.quant

[66:57] File #3/6
[66:57] Loading run D:\LFQ\8600_Nano_Data\LFQ_ZenoTOF8600_ZenoSWATH_85VW_15min_Nano_50ng_Condition_B_REP1.wiff
[71:04] Pre-processing...
[71:20] 1747 MS1 and 148495 MS2 scans in 1747 (inferred) and 1747 (encoded) cycles, 7899997 precursors in range
[71:35] Calibrating with mass accuracies 29 (MS1), 25 (MS2)
[73:51] RT window set to 1.059
[73:52] Recommended MS1 mass accuracy setting: 30 ppm
[75:54] Searching decoys
[80:23] Main search
[90:17] Removing low confidence identifications
[90:57] Removing interfering precursors
[91:23] Training neural networks on 163139 target and 120802 decoy PSMs
[95:01] Training neural networks on 163139 target and 117606 decoy PSMs
[97:59] Number of IDs at 0.01 FDR: 83875
[98:01] Precursors at 1% peptidoform FDR: 80335
[98:05] Calculating protein q-values
[98:07] Number of genes identified at 1% FDR: 9253 (precursor-level), 8193 (protein-level) (inference performed using proteotypic peptides only)
[98:07] Quantification
[98:09] Precursors with scored PTMs at 1% FDR: 1833 out of 2144 considered
[98:09] Precursors with all scored PTM sites unoccupied at 1% FDR: 78502
[98:09] Precursors with PTMs localised (when required) with > 90% confidence: 973 out of 1833
[98:12] Quantification information saved to D:\LFQ\8600_Nano_Data\LFQ_ZenoTOF8600_ZenoSWATH_85VW_15min_Nano_50ng_Condition_B_REP1.wiff.quant

[98:12] File #4/6
[98:12] Loading run D:\LFQ\8600_Nano_Data\LFQ_ZenoTOF8600_ZenoSWATH_85VW_15min_Nano_50ng_Condition_A_REP3.wiff
[101:35] Pre-processing...
[101:51] 1747 MS1 and 148495 MS2 scans in 1747 (inferred) and 1747 (encoded) cycles, 7899997 precursors in range
[102:06] Calibrating with mass accuracies 29 (MS1), 25 (MS2)
[104:16] RT window set to 1.12773
[104:16] Recommended MS1 mass accuracy setting: 28 ppm
[106:16] Searching decoys
[111:11] Main search
[121:13] Removing low confidence identifications
[122:05] Removing interfering precursors
[122:31] Training neural networks on 168887 target and 126063 decoy PSMs
[125:07] Training neural networks on 168887 target and 123023 decoy PSMs
[127:15] Number of IDs at 0.01 FDR: 85277
[127:17] Precursors at 1% peptidoform FDR: 81490
[127:20] Calculating protein q-values
[127:22] Number of genes identified at 1% FDR: 9322 (precursor-level), 8255 (protein-level) (inference performed using proteotypic peptides only)
[127:22] Quantification
[127:24] Precursors with scored PTMs at 1% FDR: 1773 out of 2098 considered
[127:24] Precursors with all scored PTM sites unoccupied at 1% FDR: 79717
[127:24] Precursors with PTMs localised (when required) with > 90% confidence: 884 out of 1773
[127:26] Quantification information saved to D:\LFQ\8600_Nano_Data\LFQ_ZenoTOF8600_ZenoSWATH_85VW_15min_Nano_50ng_Condition_A_REP3.wiff.quant

[127:26] File #5/6
[127:26] Loading run D:\LFQ\8600_Nano_Data\LFQ_ZenoTOF8600_ZenoSWATH_85VW_15min_Nano_50ng_Condition_A_REP2.wiff
[131:12] Pre-processing...
[131:27] 1747 MS1 and 148495 MS2 scans in 1747 (inferred) and 1747 (encoded) cycles, 7899997 precursors in range
[131:43] Calibrating with mass accuracies 29 (MS1), 25 (MS2)
[133:58] RT window set to 1.34521
[133:58] Recommended MS1 mass accuracy setting: 28 ppm
[135:59] Searching decoys
[140:59] Main search
[150:38] Removing low confidence identifications
[151:15] Removing interfering precursors
[151:38] Training neural networks on 173700 target and 132075 decoy PSMs
[153:54] Training neural networks on 173700 target and 128337 decoy PSMs
[155:49] Number of IDs at 0.01 FDR: 85184
[155:51] Precursors at 1% peptidoform FDR: 81585
[155:54] Calculating protein q-values
[155:56] Number of genes identified at 1% FDR: 9338 (precursor-level), 8365 (protein-level) (inference performed using proteotypic peptides only)
[155:56] Quantification
[155:57] Precursors with scored PTMs at 1% FDR: 1681 out of 2004 considered
[155:57] Precursors with all scored PTM sites unoccupied at 1% FDR: 79904
[155:57] Precursors with PTMs localised (when required) with > 90% confidence: 827 out of 1681
[155:59] Quantification information saved to D:\LFQ\8600_Nano_Data\LFQ_ZenoTOF8600_ZenoSWATH_85VW_15min_Nano_50ng_Condition_A_REP2.wiff.quant

[156:00] File #6/6
[156:00] Loading run D:\LFQ\8600_Nano_Data\LFQ_ZenoTOF8600_ZenoSWATH_85VW_15min_Nano_50ng_Condition_A_REP1.wiff
[158:47] Pre-processing...
[158:58] 1747 MS1 and 148495 MS2 scans in 1747 (inferred) and 1747 (encoded) cycles, 7899997 precursors in range
[159:10] Calibrating with mass accuracies 29 (MS1), 25 (MS2)
[160:39] RT window set to 1.12244
[160:39] Recommended MS1 mass accuracy setting: 32 ppm
[162:04] Searching decoys
[165:43] Main search
[173:07] Removing low confidence identifications
[173:45] Removing interfering precursors
[174:08] Training neural networks on 166950 target and 123294 decoy PSMs
[176:15] Training neural networks on 166950 target and 119246 decoy PSMs
[178:01] Number of IDs at 0.01 FDR: 84839
[178:03] Precursors at 1% peptidoform FDR: 81333
[178:06] Calculating protein q-values
[178:07] Number of genes identified at 1% FDR: 9270 (precursor-level), 8272 (protein-level) (inference performed using proteotypic peptides only)
[178:07] Quantification
[178:09] Precursors with scored PTMs at 1% FDR: 1704 out of 1990 considered
[178:09] Precursors with all scored PTM sites unoccupied at 1% FDR: 79629
[178:09] Precursors with PTMs localised (when required) with > 90% confidence: 850 out of 1704
[178:11] Quantification information saved to D:\LFQ\8600_Nano_Data\LFQ_ZenoTOF8600_ZenoSWATH_85VW_15min_Nano_50ng_Condition_A_REP1.wiff.quant

[178:11] Cross-run analysis
[178:11] Reading quantification information: 6 files
[178:59] Quantifying peptides
[179:41] Assembling protein groups
[179:46] Quantifying proteins
[179:46] Calculating q-values for protein and gene groups
[179:53] Calculating global q-values for protein and gene groups
[179:53] Protein groups with global q-value <= 0.01: 9858
[179:58] Compressed report saved to Q:\ACTIVE\2024\LFQbenchmark_2ndgeneration\DATA_ANALYSIS\DIANN\ZenoTOF8600\251028_DIANNv210_Proteobench_ZenoSWATH_AB_3replicates_report-first-pass.parquet. Use R 'arrow' or Python 'PyArrow' package to process
[179:58] Saving precursor levels matrix
[180:02] Precursor levels matrix (1% precursor and protein group FDR) saved to Q:\ACTIVE\2024\LFQbenchmark_2ndgeneration\DATA_ANALYSIS\DIANN\ZenoTOF8600\251028_DIANNv210_Proteobench_ZenoSWATH_AB_3replicates_report-first-pass.pr_matrix.tsv.
[180:02] Manifest saved to Q:\ACTIVE\2024\LFQbenchmark_2ndgeneration\DATA_ANALYSIS\DIANN\ZenoTOF8600\251028_DIANNv210_Proteobench_ZenoSWATH_AB_3replicates_report-first-pass.manifest.txt
[180:02] Stats report saved to Q:\ACTIVE\2024\LFQbenchmark_2ndgeneration\DATA_ANALYSIS\DIANN\ZenoTOF8600\251028_DIANNv210_Proteobench_ZenoSWATH_AB_3replicates_report-first-pass.stats.tsv
[180:02] Generating spectral library:
[180:05] 106300 target and 1092 decoy precursors saved
[180:06] Spectral library saved to Q:\ACTIVE\2024\LFQbenchmark_2ndgeneration\DATA_ANALYSIS\DIANN\ZenoTOF8600\251028_DIANNv210_Proteobench_ZenoSWATH_AB_3replicates_report-lib.parquet

[180:06] Loading spectral library Q:\ACTIVE\2024\LFQbenchmark_2ndgeneration\DATA_ANALYSIS\DIANN\ZenoTOF8600\251028_DIANNv210_Proteobench_ZenoSWATH_AB_3replicates_report-lib.parquet
[180:09] Spectral library loaded: 11756 protein isoforms, 11588 protein groups and 107392 precursors in 92166 elution groups.
[180:09] Initialising library
[180:10] Saving the library to Q:\ACTIVE\2024\LFQbenchmark_2ndgeneration\DATA_ANALYSIS\DIANN\ZenoTOF8600\251028_DIANNv210_Proteobench_ZenoSWATH_AB_3replicates_report-lib.parquet.skyline.speclib


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

[180:14] File #1/6
[180:14] Loading run D:\LFQ\8600_Nano_Data\LFQ_ZenoTOF8600_ZenoSWATH_85VW_15min_Nano_50ng_Condition_B_REP3.wiff
[183:09] Pre-processing...
[183:12] 1747 MS1 and 148495 MS2 scans in 1747 (inferred) and 1747 (encoded) cycles, 106300 precursors in range
[183:12] Calibrating with mass accuracies 29 (MS1), 25 (MS2)
[183:13] RT window set to 0.310036
[183:13] Recommended MS1 mass accuracy setting: 36 ppm
[183:14] Searching decoys
[183:15] Main search
[183:19] Removing low confidence identifications
[183:26] Removing interfering precursors
[183:27] Training neural networks on 87972 target and 53732 decoy PSMs
[184:16] Training neural networks on 87958 target and 56949 decoy PSMs
[185:05] Number of IDs at 0.01 FDR: 93043
[185:05] Precursors at 1% peptidoform FDR: 90152
[185:06] No protein annotation, skipping protein q-value calculation
[185:06] Quantification
[185:07] Precursors with scored PTMs at 1% FDR: 2091 out of 2201 considered
[185:07] Precursors with all scored PTM sites unoccupied at 1% FDR: 88061
[185:07] Precursors with PTMs localised (when required) with > 90% confidence: 1096 out of 2091

[185:08] File #2/6
[185:08] Loading run D:\LFQ\8600_Nano_Data\LFQ_ZenoTOF8600_ZenoSWATH_85VW_15min_Nano_50ng_Condition_B_REP2.wiff
[188:01] Pre-processing...
[188:04] 1747 MS1 and 148495 MS2 scans in 1747 (inferred) and 1747 (encoded) cycles, 106300 precursors in range
[188:04] Calibrating with mass accuracies 29 (MS1), 25 (MS2)
[188:05] RT window set to 0.303134
[188:05] Recommended MS1 mass accuracy setting: 32 ppm
[188:06] Searching decoys
[188:07] Main search
[188:11] Removing low confidence identifications
[188:17] Removing interfering precursors
[188:19] Training neural networks on 87715 target and 53251 decoy PSMs
[189:08] Training neural networks on 87695 target and 56510 decoy PSMs
[189:57] Number of IDs at 0.01 FDR: 93269
[189:57] Precursors at 1% peptidoform FDR: 90041
[189:57] No protein annotation, skipping protein q-value calculation
[189:57] Quantification
[189:58] Precursors with scored PTMs at 1% FDR: 2113 out of 2214 considered
[189:58] Precursors with all scored PTM sites unoccupied at 1% FDR: 87928
[189:58] Precursors with PTMs localised (when required) with > 90% confidence: 1098 out of 2113

[190:00] File #3/6
[190:00] Loading run D:\LFQ\8600_Nano_Data\LFQ_ZenoTOF8600_ZenoSWATH_85VW_15min_Nano_50ng_Condition_B_REP1.wiff
[192:50] Pre-processing...
[192:53] 1747 MS1 and 148495 MS2 scans in 1747 (inferred) and 1747 (encoded) cycles, 106300 precursors in range
[192:53] Calibrating with mass accuracies 29 (MS1), 25 (MS2)
[192:54] RT window set to 0.308777
[192:54] Recommended MS1 mass accuracy setting: 33 ppm
[192:55] Searching decoys
[192:56] Main search
[193:00] Removing low confidence identifications
[193:06] Removing interfering precursors
[193:07] Training neural networks on 87781 target and 53720 decoy PSMs
[193:57] Training neural networks on 87762 target and 56787 decoy PSMs
[194:47] Number of IDs at 0.01 FDR: 93316
[194:47] Precursors at 1% peptidoform FDR: 90700
[194:47] No protein annotation, skipping protein q-value calculation
[194:47] Quantification
[194:48] Precursors with scored PTMs at 1% FDR: 2109 out of 2213 considered
[194:48] Precursors with all scored PTM sites unoccupied at 1% FDR: 88591
[194:48] Precursors with PTMs localised (when required) with > 90% confidence: 1092 out of 2109

[194:49] File #4/6
[194:49] Loading run D:\LFQ\8600_Nano_Data\LFQ_ZenoTOF8600_ZenoSWATH_85VW_15min_Nano_50ng_Condition_A_REP3.wiff
[197:38] Pre-processing...
[197:41] 1747 MS1 and 148495 MS2 scans in 1747 (inferred) and 1747 (encoded) cycles, 106300 precursors in range
[197:41] Calibrating with mass accuracies 29 (MS1), 25 (MS2)
[197:42] RT window set to 0.309748
[197:42] Recommended MS1 mass accuracy setting: 33 ppm
[197:43] Searching decoys
[197:45] Main search
[197:48] Removing low confidence identifications
[197:55] Removing interfering precursors
[197:57] Training neural networks on 87827 target and 53797 decoy PSMs
[198:46] Training neural networks on 87815 target and 56862 decoy PSMs
[199:35] Number of IDs at 0.01 FDR: 93276
[199:35] Precursors at 1% peptidoform FDR: 90346
[199:35] No protein annotation, skipping protein q-value calculation
[199:35] Quantification
[199:36] Precursors with scored PTMs at 1% FDR: 2056 out of 2169 considered
[199:36] Precursors with all scored PTM sites unoccupied at 1% FDR: 88290
[199:36] Precursors with PTMs localised (when required) with > 90% confidence: 1039 out of 2056

[199:37] File #5/6
[199:37] Loading run D:\LFQ\8600_Nano_Data\LFQ_ZenoTOF8600_ZenoSWATH_85VW_15min_Nano_50ng_Condition_A_REP2.wiff
[202:29] Pre-processing...
[202:32] 1747 MS1 and 148495 MS2 scans in 1747 (inferred) and 1747 (encoded) cycles, 106300 precursors in range
[202:32] Calibrating with mass accuracies 29 (MS1), 25 (MS2)
[202:33] RT window set to 0.307871
[202:33] Recommended MS1 mass accuracy setting: 34 ppm
[202:34] Searching decoys
[202:35] Main search
[202:39] Removing low confidence identifications
[202:45] Removing interfering precursors
[202:47] Training neural networks on 87989 target and 53615 decoy PSMs
[203:36] Training neural networks on 87976 target and 56690 decoy PSMs
[204:26] Number of IDs at 0.01 FDR: 93383
[204:26] Precursors at 1% peptidoform FDR: 90440
[204:26] No protein annotation, skipping protein q-value calculation
[204:26] Quantification
[204:27] Precursors with scored PTMs at 1% FDR: 2039 out of 2174 considered
[204:27] Precursors with all scored PTM sites unoccupied at 1% FDR: 88401
[204:27] Precursors with PTMs localised (when required) with > 90% confidence: 1044 out of 2039

[204:28] File #6/6
[204:28] Loading run D:\LFQ\8600_Nano_Data\LFQ_ZenoTOF8600_ZenoSWATH_85VW_15min_Nano_50ng_Condition_A_REP1.wiff
[207:19] Pre-processing...
[207:22] 1747 MS1 and 148495 MS2 scans in 1747 (inferred) and 1747 (encoded) cycles, 106300 precursors in range
[207:22] Calibrating with mass accuracies 29 (MS1), 25 (MS2)
[207:22] RT window set to 0.309718
[207:23] Recommended MS1 mass accuracy setting: 31 ppm
[207:23] Searching decoys
[207:25] Main search
[207:29] Removing low confidence identifications
[207:35] Removing interfering precursors
[207:37] Training neural networks on 88025 target and 53968 decoy PSMs
[208:27] Training neural networks on 88008 target and 57010 decoy PSMs
[209:16] Number of IDs at 0.01 FDR: 93180
[209:16] Precursors at 1% peptidoform FDR: 90711
[209:16] No protein annotation, skipping protein q-value calculation
[209:16] Quantification
[209:17] Precursors with scored PTMs at 1% FDR: 2021 out of 2131 considered
[209:17] Precursors with all scored PTM sites unoccupied at 1% FDR: 88690
[209:17] Precursors with PTMs localised (when required) with > 90% confidence: 1022 out of 2021

[209:18] Cross-run analysis
[209:18] Reading quantification information: 6 files
[209:21] Quantifying peptides
[211:58] Quantification parameters: 0.309242, 0.0589985, 0.25476, 0.01289, 0.137825, 0.0869565, 0.0486023, 0.0821508, 0.0563714, 0.0559816, 0.0461127, 0.0482828, 0.283212, 0.0938022, 0.120998, 0.0115754
[212:01] Quantifying proteins
[212:01] Calculating q-values for protein and gene groups
[212:01] Calculating global q-values for protein and gene groups
[212:01] Protein groups with global q-value <= 0.01: 9517
[212:07] Compressed report saved to Q:\ACTIVE\2024\LFQbenchmark_2ndgeneration\DATA_ANALYSIS\DIANN\ZenoTOF8600\251028_DIANNv210_Proteobench_ZenoSWATH_AB_3replicates_report.parquet. Use R 'arrow' or Python 'PyArrow' package to process
[212:07] Saving precursor levels matrix
[212:11] Precursor levels matrix (1% precursor and protein group FDR) saved to Q:\ACTIVE\2024\LFQbenchmark_2ndgeneration\DATA_ANALYSIS\DIANN\ZenoTOF8600\251028_DIANNv210_Proteobench_ZenoSWATH_AB_3replicates_report.pr_matrix.tsv.
[212:11] Saving protein group levels matrix
[212:11] Protein groups matrix saved to Q:\ACTIVE\2024\LFQbenchmark_2ndgeneration\DATA_ANALYSIS\DIANN\ZenoTOF8600\251028_DIANNv210_Proteobench_ZenoSWATH_AB_3replicates_report.pg_matrix.tsv.
[212:11] Saving gene group levels matrix
[212:11] Gene groups matrix saved to Q:\ACTIVE\2024\LFQbenchmark_2ndgeneration\DATA_ANALYSIS\DIANN\ZenoTOF8600\251028_DIANNv210_Proteobench_ZenoSWATH_AB_3replicates_report.gg_matrix.tsv.
[212:11] Saving unique genes levels matrix
[212:12] Unique genes matrix saved to Q:\ACTIVE\2024\LFQbenchmark_2ndgeneration\DATA_ANALYSIS\DIANN\ZenoTOF8600\251028_DIANNv210_Proteobench_ZenoSWATH_AB_3replicates_report.unique_genes_matrix.tsv.
[212:12] Manifest saved to Q:\ACTIVE\2024\LFQbenchmark_2ndgeneration\DATA_ANALYSIS\DIANN\ZenoTOF8600\251028_DIANNv210_Proteobench_ZenoSWATH_AB_3replicates_report.manifest.txt
[212:12] Stats report saved to Q:\ACTIVE\2024\LFQbenchmark_2ndgeneration\DATA_ANALYSIS\DIANN\ZenoTOF8600\251028_DIANNv210_Proteobench_ZenoSWATH_AB_3replicates_report.stats.tsv

