Diann Logs

assemble_small1_assemble_empirical_library.log

DIA-NN 1.8.1 (Data-Independent Acquisition by Neural Networks)
Compiled on Apr 15 2022 08:45:18
Current date and time: Mon Aug 14 19:34:42 2023
Logical CPU cores: 4
Thread number set to 4
The spectral library (if generated) will retain the original spectra but will include empirically-aligned RTs
Existing .quant files will be used
A fast algorithm will be used to select the MS2 mass accuracy setting
Mass accuracy will be determined separately for different runs
Scan windows will be inferred separately for different runs
A spectral library will be generated
DIA-NN will optimise the mass accuracy separately for each 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.

4 files will be processed
[0:00] Loading spectral library Human_Library.tsv.speclib
[0:00] Library annotated with sequence database(s): C:\Users\Admin\Desktop\Spectral_Libraries_Database\_ip2_ip2_data_paser_database__UniProt_human_contaminant_10_21_2021_reversed.fasta
[0:00] Protein names missing for some isoforms
[0:00] Gene names missing for some isoforms
[0:00] Library contains 13072 proteins, and 13040 genes
[0:01] Spectral library loaded: 13103 protein isoforms, 13103 protein groups and 573610 precursors in 513182 elution groups.
[0:01] Initialising library

[0:01] Cross-run analysis
[0:01] Reading quantification information: 4 files
[0:02] Averaged recommended settings for this experiment: Mass accuracy = 13ppm, MS1 accuracy = 18ppm, Scan window = 11
[0:02] Quantifying peptides
[0:03] Assembling protein groups
[0:03] Quantifying proteins
[0:04] Calculating q-values for protein and gene groups
[0:04] Calculating global q-values for protein and gene groups
[0:04] Writing report
[0:09] Report saved to report.tsv.
[0:09] Stats report saved to report.stats.tsv
[0:09] Generating spectral library:
[0:10] Saving spectral library to empirical_library.tsv
[0:15] 41283 precursors saved
[0:15] Loading the generated library and saving it in the .speclib format
[0:15] Loading spectral library empirical_library.tsv
[0:17] Spectral library loaded: 5288 protein isoforms, 5288 protein groups and 41283 precursors in 38942 elution groups.
[0:17] Protein names missing for some isoforms
[0:17] Gene names missing for some isoforms
[0:17] Library contains 0 proteins, and 0 genes
[0:17] Saving the library to empirical_library.tsv.speclib
[0:17] Log saved to report.log.txt
Finished

assemble_small2_assemble_empirical_library.log

DIA-NN 1.8.1 (Data-Independent Acquisition by Neural Networks)
Compiled on Apr 15 2022 08:45:18
Current date and time: Mon Aug 14 19:32:57 2023
Logical CPU cores: 4
Thread number set to 4
The spectral library (if generated) will retain the original spectra but will include empirically-aligned RTs
Existing .quant files will be used
A fast algorithm will be used to select the MS2 mass accuracy setting
Mass accuracy will be determined separately for different runs
Scan windows will be inferred separately for different runs
A spectral library will be generated
DIA-NN will optimise the mass accuracy separately for each 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.

4 files will be processed
[0:00] Loading spectral library Human_Library.tsv.speclib
[0:00] Library annotated with sequence database(s): C:\Users\Admin\Desktop\Spectral_Libraries_Database\_ip2_ip2_data_paser_database__UniProt_human_contaminant_10_21_2021_reversed.fasta
[0:00] Protein names missing for some isoforms
[0:00] Gene names missing for some isoforms
[0:00] Library contains 13072 proteins, and 13040 genes
[0:00] Spectral library loaded: 13103 protein isoforms, 13103 protein groups and 573610 precursors in 513182 elution groups.
[0:00] Initialising library

[0:01] Cross-run analysis
[0:01] Reading quantification information: 4 files
[0:02] Averaged recommended settings for this experiment: Mass accuracy = 13ppm, MS1 accuracy = 19ppm, Scan window = 11
[0:02] Quantifying peptides
[0:03] Assembling protein groups
[0:03] Quantifying proteins
[0:04] Calculating q-values for protein and gene groups
[0:04] Calculating global q-values for protein and gene groups
[0:04] Writing report
[0:09] Report saved to report.tsv.
[0:09] Stats report saved to report.stats.tsv
[0:09] Generating spectral library:
[0:09] Saving spectral library to empirical_library.tsv
[0:15] 41283 precursors saved
[0:15] Loading the generated library and saving it in the .speclib format
[0:15] Loading spectral library empirical_library.tsv
[0:17] Spectral library loaded: 5288 protein isoforms, 5288 protein groups and 41283 precursors in 38942 elution groups.
[0:17] Protein names missing for some isoforms
[0:17] Gene names missing for some isoforms
[0:17] Library contains 0 proteins, and 0 genes
[0:17] Saving the library to empirical_library.tsv.speclib
[0:17] Log saved to report.log.txt
Finished

indiv_final1_REDACTED_H5_DIA_final_diann.log

DIA-NN 1.8.1 (Data-Independent Acquisition by Neural Networks)
Compiled on Apr 15 2022 08:45:18
Current date and time: Mon Aug 14 20:31:38 2023
Logical CPU cores: 16
Thread number set to 12
Scan window radius set to 11
Interference removal from fragment elution curves disabled
Main report will not be generated
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
Implicit protein grouping: genes; this determines which peptides are considered 'proteotypic' and thus affects protein FDR calculation
Mass accuracy will be fixed to 1.3e-05 (MS2) and 1.8e-05 (MS1)

1 files will be processed
[0:00] Loading spectral library empirical_library.tsv
[0:01] Spectral library loaded: 5288 protein isoforms, 5288 protein groups and 41283 precursors in 38942 elution groups.
[0:01] Loading protein annotations from FASTA uniprot_human_sp_isoforms_2022-03-15_crap.fasta
[0:02] Annotating library proteins with information from the FASTA database
[0:02] Protein names missing for some isoforms
[0:02] Gene names missing for some isoforms
[0:02] Library contains 5263 proteins, and 5256 genes
[0:02] Initialising library
[0:02] Saving the library to empirical_library.tsv.speclib

[0:02] File #1/1
[0:02] Loading run REDACTED_H5_DIA.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.
[0:14] Detected MS/MS range: 99.9992 - 1600
[0:15] Run loaded
[0:15] 41283 library precursors are potentially detectable
[0:15] Processing batch #1 out of 20 
[0:15] Precursor search
[0:18] Optimising weights
Averages: 
0.191774 0.104693 0 0.112132 0.280742 0.228411 
Weights: 
7.64426 0 0 0 2.42156 0.675306 
[0:18] Calculating q-values
[0:18] Number of IDs at 50%, 5%, 1%, 0.1% FDR: 1765, 1148, 779, 0
[0:18] Calibrating retention times
[0:18] 779 precursors used for iRT estimation.
[0:18] Precursor search
[0:18] Optimising weights
Averages: 
0.195967 0.109025 0 0.111708 0.258641 0.210368 
Weights: 
7.65716 0 0 0 2.56193 0.75373 
[0:18] Calculating q-values
[0:18] Number of IDs at 50%, 5%, 1%, 0.1% FDR: 1760, 1133, 783, 0
[0:18] Precursor search
[0:18] Optimising weights
Averages: 
0.196664 0.10961 0 0.112318 0.257333 0.207425 
Weights: 
7.75526 0 0 0 2.51205 0.722848 
[0:18] Calculating q-values
[0:18] Number of IDs at 50%, 5%, 1%, 0.1% FDR: 1762, 1135, 781, 0
[0:18] Precursor search
[0:18] Optimising weights
Averages: 
0.196355 0.109208 0 0.111796 0.257634 0.209638 
Weights: 
7.71497 0 0 0 2.52621 0.743352 
[0:18] Calculating q-values
[0:18] Number of IDs at 50%, 5%, 1%, 0.1% FDR: 1760, 1131, 780, 0
[0:18] Calibrating retention times
[0:18] 780 precursors used for iRT estimation.
[0:18] RT window set to 0.435176
[0:18] Ion mobility window set to 0.0109115
[0:18] Mass correction transform (1123 precursors): -6.74773e-09 -0.00450776 1.31872e-05 
[0:18] M/z SD: 3.84298 ppm
[0:18] Top 70% mass accuracy: 4.84519 ppm
[0:18] Top 70% mass accuracy without correction: 5.22363ppm
[0:18] MS1 mass correction transform (870 precursors): 1.58833e-09 -0.00397722 9.07857e-06 
[0:18] Top 70% MS1 mass accuracy: 3.46001 ppm
[0:18] Top 70% MS1 mass accuracy without correction: 4.85089ppm
[0:18] Refining mass correction
[0:18] Calibrating retention times
[0:18] Mass correction transform (787 precursors): -7.08676e-09 -0.00463704 1.37197e-05 
[0:18] M/z SD: 3.84645 ppm
[0:18] Top 70% mass accuracy: 4.82966 ppm
[0:18] Top 70% mass accuracy without correction: 5.22363ppm
[0:18] MS1 mass correction transform (609 precursors): -2.81926e-09 -0.00444829 1.2298e-05 
[0:18] Top 70% MS1 mass accuracy: 3.29002 ppm
[0:18] Top 70% MS1 mass accuracy without correction: 4.85089ppm
[0:18] Recommended MS1 mass accuracy setting: 16.4501 ppm
[0:18] Using mass accuracy 1.3e-05, 1.8e-05 (MS2, MS1)
[0:18] Processing batch #1 out of 20 
[0:18] Precursor search
[0:18] Optimising weights
Averages: 
0.426715 0.211012 0 0.205471 0.413222 0.514205 
Weights: 
5.44228 0 0 0 4.61817 4.83812 
[0:18] Calculating q-values
[0:18] Number of IDs at 50%, 5%, 1%, 0.1% FDR: 2030, 1808, 1519, 1039
[0:18] Calculating q-values
[0:18] Number of IDs at 50%, 5%, 1%, 0.1% FDR: 2031, 1890, 1733, 1043
[0:18] Calibrating retention times
[0:18] 1733 precursors used for iRT estimation.
[0:18] Precursor search
[0:18] Optimising weights
Averages: 
0.424496 0.208779 0 0.205183 0.428885 0.520871 0 0 0 0 |***| 0 0 0 0 0 0 0 0 0.337684 0.36023 |***| 1.4561 0.322805 2.09314 0.360968 -2.86603 1.82136 0.207502 0.594685 0.257681 0.154084 |***| 0.253732 0.169911 0.150733 0 0.0707876 0.328461 0 0 0.541193 0.437092 |***| 0.741669 0.710658 0.444499 -0.066936 -0.0209755 -0.0026827 
Weights: 
1e-09 0 0 0 0 1.35835 0 0 0 0 |***| 0 0 0 0 0 0 0 0 0 4.70229 |***| 0 0.407963 0.112513 4.29553 0 0.145052 0.523586 0 0.676038 0 |***| 1.19858 0 0 0 0 0 0 0 0.00203645 0 |***| 0 0 0 -24.0517 -7.01093 -1.78228 
[0:18] Calculating q-values
[0:18] Number of IDs at 50%, 5%, 1%, 0.1% FDR: 2025, 1893, 1678, 1356
[0:18] Calibrating retention times
[0:18] 1678 precursors used for iRT estimation.
[0:18] Precursor search
[0:18] Optimising weights
Averages: 
0.431855 0.209667 0 0.208825 0.42574 0.525566 0 0 0 0 |***| 0 0 0 0 0 0 0 0 0.344261 0.358428 |***| 1.47171 0.321654 2.08445 0.356039 -2.90591 1.41389 0.213718 0.619157 0.255706 0.151132 |***| 0.256105 0.169664 0.149881 0 0.0819276 0.334585 0 0 0.529032 0.424032 |***| 0.722977 0.690673 0.429582 -0.0602267 -0.021367 -0.00196122 
Weights: 
1e-09 0 0 0 0 1.16276 0 0 0 0 |***| 0 0 0 0 0 0 0 0 0 3.85184 |***| 0 0.665322 0.0587368 5.55931 0 0.119473 0.797631 0 1.01475 0 |***| 2.11974 0 0 0 0 0 0 0 0 0 |***| 0 0 0 -29.9438 -11.9567 -1.53039 
[0:18] Calculating q-values
[0:18] Number of IDs at 50%, 5%, 1%, 0.1% FDR: 2029, 1906, 1701, 1455
[0:18] Calibrating retention times
[0:18] 1701 precursors used for iRT estimation.
[0:18] Precursor search
[0:18] Optimising weights
Averages: 
0.43226 0.209011 0 0.208888 0.427937 0.525071 0 0 0 0 |***| 0 0 0 0 0 0 0 0 0.344749 0.360941 |***| 1.47634 0.322444 2.08884 0.357266 -2.88326 1.52968 0.211311 0.623244 0.254665 0.149627 |***| 0.255435 0.168682 0.150259 0 0.0797233 0.333865 0 0 0.528453 0.423149 |***| 0.722313 0.690373 0.432612 -0.0591907 -0.0222345 -0.00198715 
Weights: 
1e-09 0 0 0 0.044075 1.11076 0 0 0 0 |***| 0 0 0 0 0 0 0 0 0 3.8719 |***| 0 0.769999 0.0537585 5.5176 0 0.119159 0.839818 0 0.91821 0 |***| 1.88271 0 0 0 0 0 0 0 0.116486 0 |***| 0 0 0 -29.6105 -14.0425 -1.2033 
[0:18] Calculating q-values
[0:18] Number of IDs at 50%, 5%, 1%, 0.1% FDR: 2029, 1907, 1679, 1445
[0:18] Calibrating retention times
[0:18] 1679 precursors used for iRT estimation.
[0:18] Precursor search
[0:18] Optimising weights
Averages: 
0.432273 0.209367 0 0.209024 0.428532 0.52576 0 0 0 0 |***| 0 0 0 0 0 0 0 0 0.344647 0.361239 |***| 1.47963 0.322724 2.08505 0.357727 -2.87718 1.51783 0.212248 0.623597 0.256092 0.149242 |***| 0.255513 0.169114 0.150516 0 0.0800899 0.334206 0 0 0.52834 0.423761 |***| 0.722711 0.690664 0.432126 -0.0592254 -0.0223316 -0.0019832 
Weights: 
1e-09 0 0 0 0 1.16191 0 0 0 0 |***| 0 0 0 0 0 0 0 0 0 3.88035 |***| 0 0.828743 0.029384 5.52507 0 0.115103 0.897046 0 1.01031 0 |***| 2.04919 0 0 0 0 0 0 0 0.0358176 0 |***| 0 0 0 -29.1626 -13.8303 -1.00267 
[0:18] Calculating q-values
[0:18] Number of IDs at 50%, 5%, 1%, 0.1% FDR: 2027, 1890, 1725, 1191
[0:18] Calculating q-values
[0:18] Number of IDs at 50%, 5%, 1%, 0.1% FDR: 2029, 1907, 1679, 1402
[0:18] Calibrating retention times
[0:18] 1679 precursors used for iRT estimation.
[0:18] Restoring classifier and weights to
1e-09 0 0 0 0 1.16276 0 0 0 0 |***| 0 0 0 0 0 0 0 0 0 3.85184 |***| 0 0.665322 0.0587368 5.55931 0 0.119473 0.797631 0 1.01475 0 |***| 2.11974 0 0 0 0 0 0 0 0 0 |***| 0 0 0 -29.9438 -11.9567 -1.53039 
[0:18] Precursor search
[0:23] Optimising weights
Averages: 
0.427082 0.20039 0 0.20604 0.428481 0.520404 0 0 0 0 |***| 0 0 0 0 0 0 0 0 0.340348 0.358423 |***| 1.43672 0.316911 2.02217 0.359193 -2.9299 1.63026 0.219819 0.632149 0.253881 0.155053 |***| 0.252108 0.162711 0.144651 0.430611 0.0803187 0.329745 0.0547667 0.714414 0.525413 0.427877 |***| 0.727022 0.698749 0.437388 -0.0583719 -0.0208199 -0.00183358 
Weights: 
1e-09 0 0 0 0 1.01497 0 0 0 0 |***| 0 0 0 0 0 0 0 0 0 3.52789 |***| 0 1.00307 0 4.96507 0 0.0940649 1.034 0 0.470428 0 |***| 2.14729 0 0 0 0 0 0 0 0.445741 0 |***| 0 0 0 -23.4376 -11.4942 -5.78702 
[0:23] Calculating q-values
[0:23] Number of IDs at 50%, 5%, 1%, 0.1% FDR: 40427, 37430, 33054, 21262
[0:23] Calculating q-values
[0:23] Number of IDs at 50%, 5%, 1%, 0.1% FDR: 40493, 37735, 33556, 24630
[0:23] Removing low confidence identifications
[0:23] Removing interfering precursors
[0:24] Calculating q-values
[0:24] Number of IDs at 50%, 5%, 1%, 0.1% FDR: 39446, 36793, 32752, 24224
[0:24] Calibrating retention times
[0:24] 32752 precursors used for iRT estimation.
[0:25] Optimising weights
Averages: 
0.428867 0.201392 0 0.207206 0.431391 0.52337 0 0 0 0 |***| 0 0 0 0 0 0 0 0 0.341639 0.359646 |***| 1.44471 0.318319 2.02495 0.360997 -2.94794 1.60562 0.220856 0.635042 0.255492 0.155372 |***| 0.253635 0.163705 0.145421 0.437077 0.0806949 0.331056 0.0545241 0.718015 0.532482 0.434443 |***| 0.730573 0.707672 0.444131 -0.0584957 -0.0209168 -0.0020271 
Weights: 
1e-09 0 0 0 0 1.00587 0 0 0 0 |***| 0 0 0 0 0 0 0 0 0 3.51556 |***| 0 0.899957 0 5.09737 0 0.0933744 1.06885 0 0.4873 0 |***| 2.26124 0 0 0 0 0 0 0 0.519602 0 |***| 0 0 0 -23.5634 -11.6585 -6.00935 
[0:25] Training neural networks: 39627 targets, 26596 decoys
[0:29] Calculating q-values
[0:29] Number of IDs at 50%, 5%, 1%, 0.1% FDR: 39606, 39105, 38355, 35500
[0:29] Calibrating retention times
[0:29] 38355 precursors used for iRT estimation.
[0:30] Calculating protein q-values
[0:30] Number of genes identified at 1% FDR: 4974 (precursor-level), 4500 (protein-level) (inference performed using proteotypic peptides only)
[0:30] Quantification
[0:31] Quantification information saved to ./REDACTED_H5_DIA_d.quant.

[0:31] Cross-run analysis
[0:31] Reading quantification information: 1 files
[0:31] Quantifying peptides
[0:31] Assembling protein groups
[0:31] Quantifying proteins
[0:31] Calculating q-values for protein and gene groups
[0:31] Calculating global q-values for protein and gene groups
[0:31] Stats report saved to report.stats.tsv
[0:31] Log saved to report.log.txt
Finished

indiv_final2_REDACTED_H5_DIA_final_diann.log

DIA-NN 1.8.1 (Data-Independent Acquisition by Neural Networks)
Compiled on Apr 15 2022 08:45:18
Current date and time: Mon Aug 14 20:29:03 2023
Logical CPU cores: 16
Thread number set to 12
Scan window radius set to 11
Interference removal from fragment elution curves disabled
Main report will not be generated
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
Implicit protein grouping: genes; this determines which peptides are considered 'proteotypic' and thus affects protein FDR calculation
Mass accuracy will be fixed to 1.3e-05 (MS2) and 1.9e-05 (MS1)

1 files will be processed
[0:00] Loading spectral library empirical_library.tsv
[0:01] Spectral library loaded: 5288 protein isoforms, 5288 protein groups and 41283 precursors in 38942 elution groups.
[0:01] Loading protein annotations from FASTA uniprot_human_sp_isoforms_2022-03-15_crap.fasta
[0:02] Annotating library proteins with information from the FASTA database
[0:02] Protein names missing for some isoforms
[0:02] Gene names missing for some isoforms
[0:02] Library contains 5263 proteins, and 5256 genes
[0:02] Initialising library
[0:02] Saving the library to empirical_library.tsv.speclib

[0:02] File #1/1
[0:02] Loading run REDACTED_H5_DIA.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.
[0:14] Detected MS/MS range: 99.9992 - 1600
[0:15] Run loaded
[0:15] 41283 library precursors are potentially detectable
[0:15] Processing batch #1 out of 20 
[0:15] Precursor search
[0:18] Optimising weights
Averages: 
0.191774 0.104693 0 0.112132 0.280742 0.228411 
Weights: 
7.64426 0 0 0 2.42156 0.675306 
[0:18] Calculating q-values
[0:18] Number of IDs at 50%, 5%, 1%, 0.1% FDR: 1765, 1148, 779, 0
[0:18] Calibrating retention times
[0:18] 779 precursors used for iRT estimation.
[0:18] Precursor search
[0:18] Optimising weights
Averages: 
0.195967 0.109025 0 0.111708 0.258641 0.210368 
Weights: 
7.65716 0 0 0 2.56193 0.75373 
[0:18] Calculating q-values
[0:18] Number of IDs at 50%, 5%, 1%, 0.1% FDR: 1760, 1133, 783, 0
[0:18] Precursor search
[0:18] Optimising weights
Averages: 
0.196664 0.10961 0 0.112318 0.257333 0.207425 
Weights: 
7.75526 0 0 0 2.51205 0.722848 
[0:18] Calculating q-values
[0:18] Number of IDs at 50%, 5%, 1%, 0.1% FDR: 1762, 1135, 781, 0
[0:18] Precursor search
[0:18] Optimising weights
Averages: 
0.196355 0.109208 0 0.111796 0.257634 0.209638 
Weights: 
7.71497 0 0 0 2.52621 0.743352 
[0:18] Calculating q-values
[0:18] Number of IDs at 50%, 5%, 1%, 0.1% FDR: 1760, 1131, 780, 0
[0:18] Calibrating retention times
[0:18] 780 precursors used for iRT estimation.
[0:18] RT window set to 0.435176
[0:18] Ion mobility window set to 0.0109115
[0:18] Mass correction transform (1123 precursors): -6.74773e-09 -0.00450776 1.31872e-05 
[0:18] M/z SD: 3.84298 ppm
[0:18] Top 70% mass accuracy: 4.84519 ppm
[0:18] Top 70% mass accuracy without correction: 5.22363ppm
[0:18] MS1 mass correction transform (870 precursors): 1.58833e-09 -0.00397722 9.07857e-06 
[0:18] Top 70% MS1 mass accuracy: 3.46001 ppm
[0:18] Top 70% MS1 mass accuracy without correction: 4.85089ppm
[0:18] Refining mass correction
[0:18] Calibrating retention times
[0:18] Mass correction transform (787 precursors): -7.08676e-09 -0.00463704 1.37197e-05 
[0:18] M/z SD: 3.84645 ppm
[0:18] Top 70% mass accuracy: 4.82966 ppm
[0:18] Top 70% mass accuracy without correction: 5.22363ppm
[0:18] MS1 mass correction transform (609 precursors): -2.81926e-09 -0.00444829 1.2298e-05 
[0:18] Top 70% MS1 mass accuracy: 3.29002 ppm
[0:18] Top 70% MS1 mass accuracy without correction: 4.85089ppm
[0:18] Recommended MS1 mass accuracy setting: 16.4501 ppm
[0:18] Using mass accuracy 1.3e-05, 1.9e-05 (MS2, MS1)
[0:18] Processing batch #1 out of 20 
[0:18] Precursor search
[0:18] Optimising weights
Averages: 
0.426807 0.2109 0 0.205905 0.413274 0.514952 
Weights: 
5.43691 0 0 0 4.56927 4.83271 
[0:18] Calculating q-values
[0:18] Number of IDs at 50%, 5%, 1%, 0.1% FDR: 2030, 1808, 1519, 1041
[0:18] Calculating q-values
[0:18] Number of IDs at 50%, 5%, 1%, 0.1% FDR: 2031, 1889, 1728, 1042
[0:18] Calibrating retention times
[0:18] 1728 precursors used for iRT estimation.
[0:18] Precursor search
[0:18] Optimising weights
Averages: 
0.424705 0.208769 0 0.205259 0.42915 0.522099 0 0 0 0 |***| 0 0 0 0 0 0 0 0 0.337689 0.360016 |***| 1.45654 0.322754 2.08777 0.361238 -2.86672 1.79155 0.208959 0.596261 0.255976 0.152768 |***| 0.253628 0.169253 0.151012 0 0.0705082 0.328598 0 0 0.542722 0.453563 |***| 0.736924 0.719933 0.468725 -0.0670302 -0.0206719 -0.00274732 
Weights: 
1e-09 0 0 0 0 1.24525 0 0 0 0 |***| 0 0 0 0 0 0 0 0 0 4.65016 |***| 0 0.277121 0.116546 4.3135 0 0.139007 0.499067 0 0.774227 0 |***| 1.27978 0 0 0 0 0 0 0 0.127562 0 |***| 0 0 0 -23.7151 -6.51065 -2.25175 
[0:18] Calculating q-values
[0:18] Number of IDs at 50%, 5%, 1%, 0.1% FDR: 2024, 1891, 1680, 1381
[0:18] Calibrating retention times
[0:18] 1680 precursors used for iRT estimation.
[0:18] Precursor search
[0:18] Optimising weights
Averages: 
0.4327 0.209494 0 0.208952 0.426733 0.52566 0 0 0 0 |***| 0 0 0 0 0 0 0 0 0.34509 0.358943 |***| 1.47565 0.322718 2.08834 0.356995 -2.91887 1.4452 0.215896 0.622545 0.253299 0.153072 |***| 0.256391 0.16974 0.149787 0 0.0819538 0.334863 0 0 0.53417 0.439401 |***| 0.716495 0.700897 0.450939 -0.0597966 -0.0213873 -0.00204455 
Weights: 
1e-09 0 0 0 0 0.94545 0 0 0 0 |***| 0 0 0 0 0 0 0 0 0 3.78293 |***| 0 0.59001 0.104727 5.52462 0 0.116881 0.760475 0 0.951332 0 |***| 1.89815 0 0 0 0 0 0 0 0.350617 0 |***| 0 0 0 -29.5245 -11.4302 -2.52525 
[0:18] Calculating q-values
[0:18] Number of IDs at 50%, 5%, 1%, 0.1% FDR: 2029, 1907, 1691, 1438
[0:18] Calibrating retention times
[0:18] 1691 precursors used for iRT estimation.
[0:18] Precursor search
[0:18] Optimising weights
Averages: 
0.433776 0.211044 0 0.210403 0.428661 0.52547 0 0 0 0 |***| 0 0 0 0 0 0 0 0 0.346197 0.362089 |***| 1.48646 0.323809 2.09374 0.357624 -2.90326 1.52159 0.212606 0.624853 0.25478 0.149639 |***| 0.256892 0.16957 0.150844 0 0.0819825 0.335165 0 0 0.532563 0.436204 |***| 0.714013 0.699301 0.452255 -0.0590465 -0.0218617 -0.00130965 
Weights: 
1e-09 0 0 0 0 0.97471 0 0 0 0 |***| 0 0 0 0 0 0 0 0 0 3.79186 |***| 0 0.814913 0.0709327 5.45169 0 0.118707 0.878022 0 0.898801 0 |***| 1.89562 0 0 0 0 0 0 0 0.373303 0 |***| 0 0 0 -28.4962 -12.7938 -0.593452 
[0:18] Calculating q-values
[0:18] Number of IDs at 50%, 5%, 1%, 0.1% FDR: 2029, 1906, 1669, 1395
[0:18] Trying the other linear classifier
[0:18] Precursor search
[0:18] Optimising weights
Averages: 
0.432242 0.210528 0 0.209467 0.427614 0.524712 0 0 0 0 |***| 0 0 0 0 0 0 0 0 0.34479 0.358982 |***| 1.47672 0.322331 2.08952 0.357474 -2.951 1.38817 0.216186 0.624221 0.252296 0.152056 |***| 0.256685 0.169857 0.150268 0 0.0823353 0.335238 0 0 0.533532 0.439277 |***| 0.713756 0.701092 0.447263 -0.0603325 -0.0216279 -0.00188116 
Weights: 
3.1196 0 0 0 0 2.3715 0 0 0 0 |***| 0 0 0 0 0 0 0 0 1.40066 0 |***| 0 0 0 8.52934 0 0.151823 1.23081 0 0.323879 0 |***| 0 0 0 0 0 0 0 0 1.07062 0 |***| 0 0 0 -45.5821 -21.017 -19.3101 
[0:18] Calculating q-values
[0:18] Number of IDs at 50%, 5%, 1%, 0.1% FDR: 2028, 1930, 1743, 1495
[0:18] Calibrating retention times
[0:18] 1743 precursors used for iRT estimation.
[0:18] Precursor search
[0:18] Optimising weights
Averages: 
0.434333 0.211534 0 0.211292 0.432752 0.516056 0 0 0 0 |***| 0 0 0 0 0 0 0 0 0.348324 0.368194 |***| 1.48289 0.32631 2.12282 0.36333 -2.97129 1.73874 0.214033 0.620962 0.257039 0.150903 |***| 0.258042 0.171093 0.150961 0 0.081783 0.339059 0 0 0.524749 0.432822 |***| 0.707229 0.690618 0.445558 -0.0574182 -0.0226361 0.000744858 
Weights: 
2.38012 0 0 0 0 2.39214 0 0 0 0 |***| 0 0 0 0 0 0 0 0 1.9203 0 |***| 0 0 0 9.07148 0 0.16156 1.27857 0 0.138981 0 |***| 0 0 0 0 0 0 0 0 1.20887 0 |***| 0 0 0 -43.4471 -23.3797 -23.1784 
[0:18] Calculating q-values
[0:18] Number of IDs at 50%, 5%, 1%, 0.1% FDR: 2026, 1892, 1714, 302
[0:18] Calculating q-values
[0:18] Number of IDs at 50%, 5%, 1%, 0.1% FDR: 2027, 1927, 1765, 1498
[0:18] Trying the other linear classifier
[0:18] Precursor search
[0:18] Optimising weights
Averages: 
0.431365 0.21028 0 0.208648 0.427431 0.524459 0 0 0 0 |***| 0 0 0 0 0 0 0 0 0.343955 0.358359 |***| 1.47335 0.321933 2.0913 0.357138 -2.93934 1.39201 0.216169 0.626222 0.252125 0.150505 |***| 0.255918 0.169087 0.150073 0 0.0824479 0.334486 0 0 0.532698 0.438373 |***| 0.712092 0.699862 0.449417 -0.0606116 -0.021654 -0.00181345 
Weights: 
1e-09 0 0 0 0 0.920278 0 0 0 0 |***| 0 0 0 0 0 0 0 0 0 3.59849 |***| 0 0.636733 0.120684 5.47226 0 0.113883 0.851406 0 0.939197 0 |***| 1.74284 0 0 0 0 0 0 0 0.333711 0 |***| 0 0 0 -29.0125 -12.6597 -2.47746 
[0:18] Calculating q-values
[0:18] Number of IDs at 50%, 5%, 1%, 0.1% FDR: 2025, 1893, 1700, 316
[0:18] Calculating q-values
[0:18] Number of IDs at 50%, 5%, 1%, 0.1% FDR: 2027, 1905, 1685, 1499
[0:18] Switching back
[0:18] Reverting weights
[0:18] Precursor search
[0:18] Optimising weights
Averages: 
0.431365 0.21028 0 0.208648 0.427431 0.524459 0 0 0 0 |***| 0 0 0 0 0 0 0 0 0.343955 0.358359 |***| 1.47335 0.321933 2.0913 0.357138 -2.93934 1.39201 0.216169 0.626222 0.252125 0.150505 |***| 0.255918 0.169087 0.150073 0 0.0824479 0.334486 0 0 0.532698 0.438373 |***| 0.712092 0.699862 0.449417 -0.0606116 -0.021654 -0.00181345 
Weights: 
2.8345 0 0 0 0 2.30692 0 0 0 0 |***| 0 0 0 0 0 0 0 0 1.67398 0 |***| 0 0 0 8.49171 0 0.149366 1.18325 0 0.393196 0 |***| 0 0 0 0 0 0 0 0 1.01666 0 |***| 0 0 0 -46.4857 -21.0611 -19.48 
[0:18] Calculating q-values
[0:18] Number of IDs at 50%, 5%, 1%, 0.1% FDR: 2025, 1893, 1700, 316
[0:18] Calculating q-values
[0:18] Number of IDs at 50%, 5%, 1%, 0.1% FDR: 2026, 1931, 1746, 1469
[0:18] Calibrating retention times
[0:18] 1746 precursors used for iRT estimation.
[0:18] Restoring classifier and weights to
1e-09 0 0 0 0 1.24525 0 0 0 0 |***| 0 0 0 0 0 0 0 0 0 4.65016 |***| 0 0.277121 0.116546 4.3135 0 0.139007 0.499067 0 0.774227 0 |***| 1.27978 0 0 0 0 0 0 0 0.127562 0 |***| 0 0 0 -23.7151 -6.51065 -2.25175 
[0:18] Precursor search
[0:23] Optimising weights
Averages: 
0.425542 0.200939 0 0.206674 0.429526 0.513252 0 0 0 0 |***| 0 0 0 0 0 0 0 0 0.34 0.356688 |***| 1.43466 0.315833 2.01024 0.360829 -2.97456 1.53257 0.220173 0.630466 0.249905 0.155951 |***| 0.252217 0.162842 0.144101 0.434592 0.0811509 0.330547 0.054471 0.71022 0.525822 0.436468 |***| 0.720972 0.70769 0.454681 -0.0594574 -0.0202947 -0.00165212 
Weights: 
2.35367 0 0 0 0 2.46766 0 0 0 0 |***| 0 0 0 0 0 0 0 0 2.59687 0 |***| 0 0 0 8.52266 0 0.135368 1.3386 0 0.00188987 0 |***| 0.474387 0 0 0 0 0 0 0 0.51741 0 |***| 0 0 0 -43.7302 -18.2915 -23.6278 
[0:24] Calculating q-values
[0:24] Number of IDs at 50%, 5%, 1%, 0.1% FDR: 40428, 37431, 32982, 20977
[0:24] Calculating q-values
[0:24] Number of IDs at 50%, 5%, 1%, 0.1% FDR: 40508, 38134, 34049, 22926
[0:24] Removing low confidence identifications
[0:24] Removing interfering precursors
[0:24] Calculating q-values
[0:25] Number of IDs at 50%, 5%, 1%, 0.1% FDR: 39450, 37187, 33237, 22568
[0:25] Calibrating retention times
[0:25] 33237 precursors used for iRT estimation.
[0:25] Optimising weights
Averages: 
0.427115 0.201948 0 0.207772 0.432319 0.515795 0 0 0 0 |***| 0 0 0 0 0 0 0 0 0.34115 0.357681 |***| 1.44252 0.317105 2.01143 0.362502 -2.99146 1.49462 0.221222 0.633268 0.25116 0.156106 |***| 0.253628 0.163682 0.144772 0.440995 0.081642 0.33168 0.0542553 0.713753 0.532738 0.442763 |***| 0.723581 0.715884 0.461323 -0.0596276 -0.0204394 -0.00185344 
Weights: 
2.33111 0 0 0 0 2.29314 0 0 0 0 |***| 0 0 0 0 0 0 0 0 2.54907 0 |***| 0 0 0 8.58307 0 0.133473 1.38899 0 0 0 |***| 0.609834 0 0 0 0 0 0 0 0.821586 0 |***| 0 0 0 -43.4317 -18.6269 -24.5978 
[0:26] Training neural networks: 39617 targets, 26593 decoys
[0:29] Calculating q-values
[0:29] Number of IDs at 50%, 5%, 1%, 0.1% FDR: 39595, 39088, 38358, 35559
[0:29] Calibrating retention times
[0:29] 38358 precursors used for iRT estimation.
[0:30] Calculating protein q-values
[0:30] Number of genes identified at 1% FDR: 4969 (precursor-level), 4471 (protein-level) (inference performed using proteotypic peptides only)
[0:30] Quantification
[0:31] Quantification information saved to ./REDACTED_H5_DIA_d.quant.

[0:31] Cross-run analysis
[0:31] Reading quantification information: 1 files
[0:31] Quantifying peptides
[0:31] Assembling protein groups
[0:31] Quantifying proteins
[0:31] Calculating q-values for protein and gene groups
[0:31] Calculating global q-values for protein and gene groups
[0:31] Stats report saved to report.stats.tsv
[0:31] Log saved to report.log.txt
Finished

indiv_prelim1_REDACTED_H5_DIA_diann.log

DIA-NN 1.8.1 (Data-Independent Acquisition by Neural Networks)
Compiled on Apr 15 2022 08:45:18
Current date and time: Mon Aug 14 19:24:34 2023
Logical CPU cores: 16
Thread number set to 12
Scan windows will be inferred separately for different runs
Only peaks with correlation sum exceeding 2 will be considered
Peaks with correlation sum below 1 from maximum will not be considered
A fast algorithm will be used to select the MS2 mass accuracy setting
Mass accuracy will be determined separately for different runs
A single score will be used until RT alignment to save memory; this can potentially lead to slower search
DIA-NN will optimise the mass accuracy separately for each 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.

1 files will be processed
[0:00] Loading spectral library Human_Library.tsv.speclib
[0:00] Library annotated with sequence database(s): C:\Users\Admin\Desktop\Spectral_Libraries_Database\_ip2_ip2_data_paser_database__UniProt_human_contaminant_10_21_2021_reversed.fasta
[0:00] Protein names missing for some isoforms
[0:00] Gene names missing for some isoforms
[0:00] Library contains 13072 proteins, and 13040 genes
[0:01] Spectral library loaded: 13103 protein isoforms, 13103 protein groups and 573610 precursors in 513182 elution groups.
[0:01] Initialising library

[0:02] File #1/1
[0:02] Loading run REDACTED_H5_DIA.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.
[0:15] Detected MS/MS range: 99.9992 - 1600
[0:16] Run loaded
[0:16] 464717 library precursors are potentially detectable
[0:17] Processing batch #1 out of 232 
[0:17] Precursor search
[0:19] Optimising weights
Averages: 
0.0694054 0.0363011 0 0.0284867 0.0779677 0.0655802 
Weights: 
5.95097 0 0 0.164228 0.812855 0.644413 
[0:19] Calculating q-values
[0:19] Number of IDs at 50%, 5%, 1%, 0.1% FDR: 208, 104, 0, 0
[0:19] Calibrating retention times
[0:19] 50 precursors used for iRT estimation.
[0:19] Precursor search
[0:19] Optimising weights
Averages: 
0.0694054 0.0363011 0 0.0284867 0.0779677 0.0655802 
Weights: 
5.95097 0 0 0.164228 0.812855 0.644413 
[0:19] Calculating q-values
[0:19] Number of IDs at 50%, 5%, 1%, 0.1% FDR: 208, 104, 0, 0
[0:19] Calibrating retention times
[0:19] 50 precursors used for iRT estimation.
[0:19] Mass correction transform (104 precursors): 6.73163e-09 -0.0010348 -1.41669e-06 
[0:19] M/z SD: 4.78415 ppm
[0:19] Top 70% mass accuracy: 6.65035 ppm
[0:19] Top 70% mass accuracy without correction: 6.73364ppm
[0:19] MS1 mass correction transform (80 precursors): 2.39117e-09 -0.00512606 9.42276e-06 
[0:19] Top 70% MS1 mass accuracy: 5.009 ppm
[0:19] Top 70% MS1 mass accuracy without correction: 5.8866ppm
[0:19] Recalibrating with mass accuracy 2e-05, 2e-05 (MS2, MS1)
[0:19] Processing batch #1 out of 232 
[0:19] Precursor search
[0:22] Optimising weights
Averages: 
0.0769758 0.0639429 0 0.0213213 0.159608 0.083126 
Weights: 
5.43253 0.131126 0 0 1.23847 0 
[0:22] Calculating q-values
[0:22] Number of IDs at 50%, 5%, 1%, 0.1% FDR: 207, 83, 0, 0
[0:22] Calibrating retention times
[0:22] 50 precursors used for iRT estimation.
[0:22] Processing batch #2 out of 232 
[0:22] Precursor search
[0:24] Optimising weights
Averages: 
0.0260997 0.0196809 0 0.0115073 0.0444281 0.0174971 
Weights: 
0.286777 0.965136 0 0.499644 0.763405 0 
[0:24] Calculating q-values
[0:24] Number of IDs at 50%, 5%, 1%, 0.1% FDR: 404, 141, 0, 0
[0:24] Calibrating retention times
[0:24] 50 precursors used for iRT estimation.
[0:24] Processing batch #3 out of 232 
[0:24] Precursor search
[0:27] Optimising weights
Averages: 
0.0889002 0.0843489 0 0.0582644 0.174176 0.112179 
Weights: 
4.78412 0 0 0 2.03847 0.69436 
[0:27] Calculating q-values
[0:27] Number of IDs at 50%, 5%, 1%, 0.1% FDR: 607, 267, 154, 0
[0:27] Calibrating retention times
[0:27] 154 precursors used for iRT estimation.
[0:27] Processing batch #4 out of 232 
[0:27] Precursor search
[0:29] Optimising weights
Averages: 
0.0897792 0.0920518 0 0.0571353 0.210825 0.0981848 
Weights: 
4.40651 0 0 0 2.07765 0.901395 
[0:29] Calculating q-values
[0:29] Number of IDs at 50%, 5%, 1%, 0.1% FDR: 792, 328, 233, 0
[0:29] Calibrating retention times
[0:29] 233 precursors used for iRT estimation.
[0:29] Processing batch #5 out of 232 
[0:29] Precursor search
[0:32] Optimising weights
Averages: 
0.0879578 0.0884934 0 0.0504536 0.221269 0.0908864 
Weights: 
4.68571 0 0 0 1.94973 0.833199 
[0:32] Calculating q-values
[0:32] Number of IDs at 50%, 5%, 1%, 0.1% FDR: 1014, 368, 252, 0
[0:32] Calibrating retention times
[0:32] 252 precursors used for iRT estimation.
[0:32] Processing batches #6-7 out of 232 
[0:32] Precursor search
[0:37] Optimising weights
Averages: 
0.0840519 0.0729612 0 0.0380492 0.229215 0.0832416 
Weights: 
4.56601 0 0 0 1.88369 0.744442 
[0:37] Calculating q-values
[0:37] Number of IDs at 50%, 5%, 1%, 0.1% FDR: 1444, 534, 329, 0
[0:37] Calibrating retention times
[0:37] 329 precursors used for iRT estimation.
[0:37] Processing batches #8-9 out of 232 
[0:37] Precursor search
[0:41] Optimising weights
Averages: 
0.0816464 0.0685845 0 0.0323445 0.209672 0.091939 
Weights: 
4.80892 0 0 0 1.71498 0.627299 
[0:41] Calculating q-values
[0:41] Number of IDs at 50%, 5%, 1%, 0.1% FDR: 1888, 628, 405, 0
[0:41] Calibrating retention times
[0:41] 405 precursors used for iRT estimation.
[0:41] Processing batches #10-11 out of 232 
[0:41] Precursor search
[0:46] Optimising weights
Averages: 
0.0828344 0.0741447 0 0.034183 0.203002 0.0735426 
Weights: 
5.00485 0 0 0 1.60078 0.600954 
[0:46] Calculating q-values
[0:46] Number of IDs at 50%, 5%, 1%, 0.1% FDR: 2370, 787, 435, 0
[0:46] Calibrating retention times
[0:46] 435 precursors used for iRT estimation.
[0:46] Processing batches #12-14 out of 232 
[0:46] Precursor search
[0:53] Optimising weights
Averages: 
0.0800823 0.0717195 0 0.0351127 0.228498 0.0764668 
Weights: 
4.6002 0 0 0 1.89494 0.565051 
[0:53] Calculating q-values
[0:54] Number of IDs at 50%, 5%, 1%, 0.1% FDR: 3071, 988, 616, 0
[0:54] Calibrating retention times
[0:54] 616 precursors used for iRT estimation.
[0:54] Processing batches #15-17 out of 232 
[0:54] Precursor search
[1:01] Optimising weights
Averages: 
0.0801631 0.067005 0 0.0311519 0.208947 0.0861766 
Weights: 
4.80237 0 0 0 1.76458 0.53594 
[1:01] Calculating q-values
[1:01] Number of IDs at 50%, 5%, 1%, 0.1% FDR: 3743, 1249, 706, 0
[1:01] Calibrating retention times
[1:01] 706 precursors used for iRT estimation.
[1:01] Precursor search
[1:01] Optimising weights
Averages: 
0.0801631 0.067005 0 0.0311519 0.208947 0.0861766 
Weights: 
4.80237 0 0 0 1.76458 0.53594 
[1:01] Calculating q-values
[1:01] Number of IDs at 50%, 5%, 1%, 0.1% FDR: 3743, 1249, 706, 0
[1:01] Precursor search
[1:01] Optimising weights
Averages: 
0.0801631 0.067005 0 0.0311519 0.208947 0.0861766 
Weights: 
4.80237 0 0 0 1.76458 0.53594 
[1:01] Calculating q-values
[1:01] Number of IDs at 50%, 5%, 1%, 0.1% FDR: 3743, 1249, 706, 0
[1:01] Precursor search
[1:02] Optimising weights
Averages: 
0.0801631 0.067005 0 0.0311519 0.208947 0.0861766 
Weights: 
4.80237 0 0 0 1.76458 0.53594 
[1:02] Calculating q-values
[1:02] Number of IDs at 50%, 5%, 1%, 0.1% FDR: 3743, 1249, 706, 0
[1:02] Calibrating retention times
[1:02] 706 precursors used for iRT estimation.
[1:02] RT window set to 0.972632
[1:02] Ion mobility window set to 0.0162288
[1:02] Peak width: 5.316
[1:02] Scan window radius set to 11
[1:02] Mass correction transform (1243 precursors): -4.05684e-09 -0.00470156 1.17403e-05 
[1:02] M/z SD: 3.72114 ppm
[1:02] Top 70% mass accuracy: 4.29712 ppm
[1:02] Top 70% mass accuracy without correction: 5.07142ppm
[1:02] MS1 mass correction transform (1088 precursors): -4.19346e-09 -0.0075217 1.99197e-05 
[1:02] Top 70% MS1 mass accuracy: 4.37167 ppm
[1:02] Top 70% MS1 mass accuracy without correction: 5.53024ppm
[1:02] Refining mass correction
[1:02] Calibrating retention times
[1:02] Mass correction transform (870 precursors): -4.09025e-09 -0.00488256 1.18806e-05 
[1:02] M/z SD: 3.72831 ppm
[1:02] Top 70% mass accuracy: 4.32579 ppm
[1:02] Top 70% mass accuracy without correction: 5.07142ppm
[1:02] MS1 mass correction transform (761 precursors): -3.55946e-09 -0.00555748 1.50915e-05 
[1:02] Top 70% MS1 mass accuracy: 3.65147 ppm
[1:02] Top 70% MS1 mass accuracy without correction: 5.53024ppm
[1:02] Recommended MS1 mass accuracy setting: 18.2574 ppm
[1:02] Suggested mass accuracy: 12.9774 ppm
[1:02] Processing batch #1 out of 232 
[1:02] Precursor search
[1:02] Optimising weights
Averages: 
0.176565 0.0866876 0 0.0711743 0.279927 0.34732 
Weights: 
1e-09 0 0 0 3.48586 3.32565 
[1:02] Calculating q-values
[1:02] Number of IDs at 50%, 5%, 1%, 0.1% FDR: 189, 132, 111, 0
[1:02] Calibrating retention times
[1:02] 111 precursors used for iRT estimation.
[1:02] Processing batch #2 out of 232 
[1:02] Precursor search
[1:03] Optimising weights
Averages: 
0.253081 0.141886 0 0.113758 0.313802 0.349912 
Weights: 
0.577907 0 0 0 3.13532 2.38639 
[1:03] Calculating q-values
[1:03] Number of IDs at 50%, 5%, 1%, 0.1% FDR: 397, 274, 214, 0
[1:03] Calibrating retention times
[1:03] 214 precursors used for iRT estimation.
[1:03] Processing batch #3 out of 232 
[1:03] Precursor search
[1:03] Optimising weights
Averages: 
0.263061 0.148849 0 0.111014 0.312555 0.337728 
Weights: 
0.751838 0 0 0 2.94548 2.27024 
[1:03] Calculating q-values
[1:03] Number of IDs at 50%, 5%, 1%, 0.1% FDR: 581, 409, 319, 0
[1:03] Calculating q-values
[1:03] Number of IDs at 50%, 5%, 1%, 0.1% FDR: 581, 416, 331, 0
[1:03] Forcing weights reset
[1:03] Precursor search
[1:03] Optimising weights
Averages: 
0.252521 0.140753 0 0.112622 0.313844 0.349912 
Weights: 
0.680364 0 0 0 3.02478 2.54921 
[1:03] Calculating q-values
[1:03] Number of IDs at 50%, 5%, 1%, 0.1% FDR: 581, 406, 315, 0
[1:03] Calibrating retention times
[1:03] 315 precursors used for iRT estimation.
[1:03] Processing batch #4 out of 232 
[1:03] Precursor search
[1:03] Optimising weights
Averages: 
0.247825 0.12802 0 0.0951671 0.308113 0.338454 
Weights: 
1.32197 0 0 0 2.61617 2.00573 
[1:03] Calculating q-values
[1:03] Number of IDs at 50%, 5%, 1%, 0.1% FDR: 777, 549, 380, 0
[1:03] Calibrating retention times
[1:03] 380 precursors used for iRT estimation.
[1:03] Processing batch #5 out of 232 
[1:03] Precursor search
[1:04] Optimising weights
Averages: 
0.239767 0.120001 0 0.0899837 0.285321 0.350149 
Weights: 
1.40588 0 0 0 2.72123 1.86783 
[1:04] Calculating q-values
[1:04] Number of IDs at 50%, 5%, 1%, 0.1% FDR: 978, 685, 457, 0
[1:04] Calibrating retention times
[1:04] 457 precursors used for iRT estimation.
[1:04] Processing batches #6-7 out of 232 
[1:04] Precursor search
[1:04] Optimising weights
Averages: 
0.237774 0.12137 0 0.0964139 0.275979 0.306917 
Weights: 
1.75122 0 0 0 2.74248 1.60774 
[1:04] Calculating q-values
[1:04] Number of IDs at 50%, 5%, 1%, 0.1% FDR: 1403, 965, 707, 0
[1:04] Calibrating retention times
[1:04] 707 precursors used for iRT estimation.
[1:04] Processing batches #8-9 out of 232 
[1:04] Precursor search
[1:05] Optimising weights
Averages: 
0.241653 0.121532 0 0.0968082 0.266776 0.338437 
Weights: 
1.50131 0 0 0 2.89251 1.87911 
[1:05] Calculating q-values
[1:05] Number of IDs at 50%, 5%, 1%, 0.1% FDR: 1834, 1272, 911, 0
[1:05] Calibrating retention times
[1:05] 911 precursors used for iRT estimation.
[1:05] Processing batches #10-11 out of 232 
[1:05] Precursor search
[1:06] Optimising weights
Averages: 
0.252257 0.132799 0 0.105511 0.274075 0.334203 
Weights: 
1.64321 0 0 0 2.69978 1.83193 
[1:06] Calculating q-values
[1:06] Number of IDs at 50%, 5%, 1%, 0.1% FDR: 2231, 1561, 1035, 0
[1:06] Calibrating retention times
[1:06] 1035 precursors used for iRT estimation.
[1:06] Processing batches #12-14 out of 232 
[1:06] Precursor search
[1:06] Optimising weights
Averages: 
0.251921 0.138014 0 0.103442 0.27242 0.314477 
Weights: 
1.82955 0 0 0 2.61063 1.72957 
[1:07] Calculating q-values
[1:07] Number of IDs at 50%, 5%, 1%, 0.1% FDR: 2844, 1983, 1324, 0
[1:07] Calculating q-values
[1:07] Number of IDs at 50%, 5%, 1%, 0.1% FDR: 2844, 1997, 1384, 0
[1:07] Calibrating retention times
[1:07] 1384 precursors used for iRT estimation.
[1:07] Processing batches #15-17 out of 232 
[1:07] Precursor search
[1:07] Optimising weights
Averages: 
0.250479 0.136141 0 0.098242 0.257565 0.339921 
Weights: 
1.62294 0 0 0 2.63768 1.95922 
[1:07] Calculating q-values
[1:07] Number of IDs at 50%, 5%, 1%, 0.1% FDR: 3466, 2466, 1577, 0
[1:07] Calibrating retention times
[1:07] 1577 precursors used for iRT estimation.
[1:07] Precursor search
[1:07] Optimising weights
Averages: 
0.253401 0.138014 0 0.103402 0.27143 0.333635 0 0 0 0 |***| 0 0 0 0 0 0 0 0 0.197795 0.213388 |***| 1.02619 0.228566 0.937578 0.210692 -1.42766 0.251812 0.0991727 0.468256 0.149541 0.158769 |***| 0.161479 0.10602 0.110246 0 0.0606558 0.193885 0 0 0.357748 0.2855 |***| 0.472656 0.49279 0.298898 -0.0322485 -0.0192714 -0.0127768 
Weights: 
1e-09 0 0 0 0 0.483624 0 0 0 0 |***| 0 0 0 0 0 0 0 0 0 1.54609 |***| 0 1.05487 0 0.662492 0 0.0120588 0 0 0.336361 0.353236 |***| 0 0 0 0 0.216698 0 0 0 0.530276 0.0511228 |***| 0 0 0 -2.1176 -5.32789 -3.07761 
[1:08] Calculating q-values
[1:08] Number of IDs at 50%, 5%, 1%, 0.1% FDR: 3452, 2566, 2025, 1382
[1:08] Calibrating retention times
[1:08] 2025 precursors used for iRT estimation.
[1:08] Precursor search
[1:08] Optimising weights
Averages: 
0.251736 0.144515 0 0.106008 0.266991 0.328693 0 0 0 0 |***| 0 0 0 0 0 0 0 0 0.196954 0.213843 |***| 1.0176 0.225306 0.981657 0.206225 -1.38198 0.11391 0.112705 0.480823 0.145973 0.165544 |***| 0.165564 0.108975 0.108909 0 0.0601664 0.199934 0 0 0.352927 0.274952 |***| 0.469272 0.489844 0.306133 -0.0317674 -0.0195138 -0.0117486 
Weights: 
1e-09 0 0 0 0.19884 0.653397 0 0 0 0 |***| 0 0 0 0 0 0 0 0 0 1.66216 |***| 0 0.67242 0 0 0.0018673 0.00530249 0.597463 0 0.0479815 0.446016 |***| 0.442057 0 0 0 0.77466 0 0 0 0.509326 0 |***| 0 0 0 -2.84668 -1.77585 -3.34589 
[1:08] Calculating q-values
[1:08] Number of IDs at 50%, 5%, 1%, 0.1% FDR: 3456, 2570, 1937, 1247
[1:08] Calibrating retention times
[1:08] 1937 precursors used for iRT estimation.
[1:08] Precursor search
[1:08] Optimising weights
Averages: 
0.245919 0.138037 0 0.0996476 0.264847 0.331103 0 0 0 0 |***| 0 0 0 0 0 0 0 0 0.190735 0.210391 |***| 0.998214 0.221527 0.956364 0.202534 -1.34132 0.261992 0.107383 0.474075 0.144912 0.156112 |***| 0.160845 0.107697 0.107161 0 0.061229 0.194394 0 0 0.347157 0.276349 |***| 0.461402 0.481016 0.299953 -0.0287405 -0.0190144 -0.0128513 
Weights: 
1e-09 0 0 0 0.682455 0.7101 0 0 0 0 |***| 0 0 0 0 0 0 0 0 0 1.92871 |***| 0 0.566995 0 0 0.0289823 0.015291 0.0968504 0 0 0.329618 |***| 0.0907483 0 0 0 0.72604 0 0 0 0.369675 0 |***| 0 0 0 -1.76586 -3.47378 -4.21532 
[1:08] Calculating q-values
[1:08] Number of IDs at 50%, 5%, 1%, 0.1% FDR: 3462, 2552, 1979, 1343
[1:08] Trying the other linear classifier
[1:08] Precursor search
[1:08] Optimising weights
Averages: 
0.24702 0.141426 0 0.103146 0.26482 0.322954 0 0 0 0 |***| 0 0 0 0 0 0 0 0 0.193195 0.212039 |***| 1.00232 0.221553 0.96357 0.203573 -1.35514 0.0607705 0.113109 0.474485 0.143969 0.163651 |***| 0.161619 0.105858 0.107601 0 0.0612918 0.197838 0 0 0.344591 0.267911 |***| 0.468582 0.48364 0.300727 -0.0297511 -0.0196837 -0.012518 
Weights: 
1e-09 0 0 0 0 1.26896 0 0 0 0 |***| 0 0 0 0 0 0 0 0 0 0.235381 |***| 0 0 0 3.22567 0 0.0380128 0.0162674 0 0 0.126379 |***| 1.625 0 0 0 0 0 0 0 0.71257 0.00493005 |***| 0.054092 0 0 -3.68272 -8.98635 -4.69421 
[1:08] Calculating q-values
[1:08] Number of IDs at 50%, 5%, 1%, 0.1% FDR: 3462, 2616, 1970, 1089
[1:08] Calibrating retention times
[1:08] 1970 precursors used for iRT estimation.
[1:08] Precursor search
[1:08] Optimising weights
Averages: 
0.249151 0.138706 0 0.104555 0.271405 0.327874 0 0 0 0 |***| 0 0 0 0 0 0 0 0 0.194505 0.21288 |***| 1.01752 0.223223 0.934656 0.208915 -1.42784 0.00529606 0.109149 0.477699 0.14718 0.158453 |***| 0.162301 0.110121 0.109374 0 0.0566207 0.195872 0 0 0.355 0.276801 |***| 0.465149 0.478944 0.303819 -0.0303036 -0.0192683 -0.0130824 
Weights: 
1e-09 0 0 0 0 1.26977 0 0 0 0 |***| 0 0 0 0 0 0 0 0 0 0.130728 |***| 0 0 0 3.2462 0 0.0327495 0 0 0 0.119878 |***| 1.72147 0 0 0 0 0 0 0 0.721742 0.0335044 |***| 0.0600051 0 0 -3.54335 -8.71169 -4.58526 
[1:08] Calculating q-values
[1:08] Number of IDs at 50%, 5%, 1%, 0.1% FDR: 3459, 2414, 1619, 1065
[1:08] Calculating q-values
[1:08] Number of IDs at 50%, 5%, 1%, 0.1% FDR: 3459, 2619, 2002, 1116
[1:08] Calibrating retention times
[1:08] 2002 precursors used for iRT estimation.
[1:08] Restoring classifier and weights to
1e-09 0 0 0 0 1.26896 0 0 0 0 |***| 0 0 0 0 0 0 0 0 0 0.235381 |***| 0 0 0 3.22567 0 0.0380128 0.0162674 0 0 0.126379 |***| 1.625 0 0 0 0 0 0 0 0.71257 0.00493005 |***| 0.054092 0 0 -3.68272 -8.98635 -4.69421 
[1:08] Precursor search
[1:59] Optimising weights
Averages: 
0.267344 0.143288 0 0.104106 0.286394 0.306038 0 0 0 0 |***| 0 0 0 0 0 0 0 0 0.216338 0.221311 |***| 1.02775 0.224464 1.02095 0.217485 -1.45489 -0.00188826 0.110309 0.489437 0.132704 0.105051 |***| 0.17199 0.111827 0.117677 0.299681 0.0507643 0.202516 0.0660939 0.481597 0.344332 0.292304 |***| 0.500732 0.510477 0.320797 -0.0295543 -0.0175834 -0.007447 
Weights: 
0.474421 0 0 0 0 1.39029 0 0 0 0 |***| 0 0 0 0 0 0 0 0 0.151485 0 |***| 0 0 0 3.23552 0.000332677 0.0437914 0 0 0 0 |***| 1.31121 0 0 0 0 0 0 0.032555 0.524411 0.0277506 |***| 0.0387024 0 0 -6.01398 -4.54831 -6.6002 
[1:59] Calculating q-values
[1:59] Number of IDs at 50%, 5%, 1%, 0.1% FDR: 48368, 34569, 23511, 7208
[1:59] Calculating q-values
[1:59] Number of IDs at 50%, 5%, 1%, 0.1% FDR: 48368, 37088, 28250, 11242
[1:59] Removing low confidence identifications
[1:59] Removing interfering precursors
[2:01] Calculating q-values
[2:01] Number of IDs at 50%, 5%, 1%, 0.1% FDR: 40904, 32243, 25637, 10428
[2:01] Calibrating retention times
[2:01] 25637 precursors used for iRT estimation.
[2:02] Optimising weights
Averages: 
0.282373 0.150139 0 0.110147 0.305302 0.327923 0 0 0 0 |***| 0 0 0 0 0 0 0 0 0.227719 0.231637 |***| 1.07215 0.231201 1.00708 0.229544 -1.59216 -0.148435 0.116974 0.514851 0.141922 0.112843 |***| 0.17966 0.117256 0.121224 0.345174 0.0490666 0.214013 0.0678151 0.525335 0.391801 0.334172 |***| 0.540674 0.575861 0.368062 -0.0331759 -0.0192861 -0.00959589 
Weights: 
0.0856069 0 0 0 0 1.02866 0 0 0 0 |***| 0 0 0 0 0 0 0 0 0 0 |***| 0 0 0 3.87892 0 0.011923 0.097252 0 0 0 |***| 0.41391 0 0 0 0 0 0 0.0666182 1.31561 0.266884 |***| 0 0.0356493 0 -7.52254 -6.3125 -10.4305 
[2:02] Training neural networks: 40904 targets, 11913 decoys
[2:05] Calculating q-values
[2:05] Number of IDs at 50%, 5%, 1%, 0.1% FDR: 40904, 34215, 31669, 26370
[2:05] Calibrating retention times
[2:05] 31669 precursors used for iRT estimation.
[2:06] Calculating protein q-values
[2:06] Number of genes identified at 1% FDR: 4605 (precursor-level), 4187 (protein-level) (inference performed using proteotypic peptides only)
[2:06] Quantification
[2:07] Quantification information saved to ./REDACTED_H5_DIA_d.quant.

[2:07] Cross-run analysis
[2:07] Reading quantification information: 1 files
[2:07] Averaged recommended settings for this experiment: Mass accuracy = 13ppm, MS1 accuracy = 18ppm, Scan window = 11
[2:07] Quantifying peptides
[2:07] Assembling protein groups
[2:08] Quantifying proteins
[2:08] Calculating q-values for protein and gene groups
[2:08] Calculating global q-values for protein and gene groups
[2:08] Writing report
[2:09] Report saved to report.tsv.
[2:09] Stats report saved to report.stats.tsv
[2:09] Log saved to report.log.txt
Finished

indiv_prelim2_REDACTED_H5_DIA_diann.log

DIA-NN 1.8.1 (Data-Independent Acquisition by Neural Networks)
Compiled on Apr 15 2022 08:45:18
Current date and time: Mon Aug 14 19:24:06 2023
Logical CPU cores: 16
Thread number set to 12
Scan windows will be inferred separately for different runs
Only peaks with correlation sum exceeding 2 will be considered
Peaks with correlation sum below 1 from maximum will not be considered
A fast algorithm will be used to select the MS2 mass accuracy setting
Mass accuracy will be determined separately for different runs
A single score will be used until RT alignment to save memory; this can potentially lead to slower search
DIA-NN will optimise the mass accuracy separately for each 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.

1 files will be processed
[0:00] Loading spectral library Human_Library.tsv.speclib
[0:00] Library annotated with sequence database(s): C:\Users\Admin\Desktop\Spectral_Libraries_Database\_ip2_ip2_data_paser_database__UniProt_human_contaminant_10_21_2021_reversed.fasta
[0:00] Protein names missing for some isoforms
[0:00] Gene names missing for some isoforms
[0:00] Library contains 13072 proteins, and 13040 genes
[0:01] Spectral library loaded: 13103 protein isoforms, 13103 protein groups and 573610 precursors in 513182 elution groups.
[0:01] Initialising library

[0:02] File #1/1
[0:02] Loading run REDACTED_H5_DIA.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.
[0:15] Detected MS/MS range: 99.9992 - 1600
[0:16] Run loaded
[0:16] 464717 library precursors are potentially detectable
[0:16] Processing batch #1 out of 232 
[0:16] Precursor search
[0:19] Optimising weights
Averages: 
0.0694054 0.0363011 0 0.0284867 0.0779677 0.0655802 
Weights: 
5.95097 0 0 0.164228 0.812855 0.644413 
[0:19] Calculating q-values
[0:19] Number of IDs at 50%, 5%, 1%, 0.1% FDR: 208, 104, 0, 0
[0:19] Calibrating retention times
[0:19] 50 precursors used for iRT estimation.
[0:19] Precursor search
[0:19] Optimising weights
Averages: 
0.0694054 0.0363011 0 0.0284867 0.0779677 0.0655802 
Weights: 
5.95097 0 0 0.164228 0.812855 0.644413 
[0:19] Calculating q-values
[0:19] Number of IDs at 50%, 5%, 1%, 0.1% FDR: 208, 104, 0, 0
[0:19] Calibrating retention times
[0:19] 50 precursors used for iRT estimation.
[0:19] Mass correction transform (104 precursors): 6.73163e-09 -0.0010348 -1.41669e-06 
[0:19] M/z SD: 4.78415 ppm
[0:19] Top 70% mass accuracy: 6.65035 ppm
[0:19] Top 70% mass accuracy without correction: 6.73364ppm
[0:19] MS1 mass correction transform (80 precursors): 2.39117e-09 -0.00512606 9.42276e-06 
[0:19] Top 70% MS1 mass accuracy: 5.009 ppm
[0:19] Top 70% MS1 mass accuracy without correction: 5.8866ppm
[0:19] Recalibrating with mass accuracy 2e-05, 2e-05 (MS2, MS1)
[0:19] Processing batch #1 out of 232 
[0:19] Precursor search
[0:21] Optimising weights
Averages: 
0.0769758 0.0639429 0 0.0213213 0.159608 0.083126 
Weights: 
5.43253 0.131126 0 0 1.23847 0 
[0:21] Calculating q-values
[0:21] Number of IDs at 50%, 5%, 1%, 0.1% FDR: 207, 83, 0, 0
[0:21] Calibrating retention times
[0:21] 50 precursors used for iRT estimation.
[0:21] Processing batch #2 out of 232 
[0:21] Precursor search
[0:24] Optimising weights
Averages: 
0.0260997 0.0196809 0 0.0115073 0.0444281 0.0174971 
Weights: 
0.286777 0.965136 0 0.499644 0.763405 0 
[0:24] Calculating q-values
[0:24] Number of IDs at 50%, 5%, 1%, 0.1% FDR: 404, 141, 0, 0
[0:24] Calibrating retention times
[0:24] 50 precursors used for iRT estimation.
[0:24] Processing batch #3 out of 232 
[0:24] Precursor search
[0:26] Optimising weights
Averages: 
0.0889002 0.0843489 0 0.0582644 0.174176 0.112179 
Weights: 
4.78412 0 0 0 2.03847 0.69436 
[0:26] Calculating q-values
[0:26] Number of IDs at 50%, 5%, 1%, 0.1% FDR: 607, 267, 154, 0
[0:26] Calibrating retention times
[0:26] 154 precursors used for iRT estimation.
[0:26] Processing batch #4 out of 232 
[0:26] Precursor search
[0:29] Optimising weights
Averages: 
0.0897792 0.0920518 0 0.0571353 0.210825 0.0981848 
Weights: 
4.40651 0 0 0 2.07765 0.901395 
[0:29] Calculating q-values
[0:29] Number of IDs at 50%, 5%, 1%, 0.1% FDR: 792, 328, 233, 0
[0:29] Calibrating retention times
[0:29] 233 precursors used for iRT estimation.
[0:29] Processing batch #5 out of 232 
[0:29] Precursor search
[0:31] Optimising weights
Averages: 
0.0879578 0.0884934 0 0.0504536 0.221269 0.0908864 
Weights: 
4.68571 0 0 0 1.94973 0.833199 
[0:31] Calculating q-values
[0:31] Number of IDs at 50%, 5%, 1%, 0.1% FDR: 1014, 368, 252, 0
[0:31] Calibrating retention times
[0:31] 252 precursors used for iRT estimation.
[0:31] Processing batches #6-7 out of 232 
[0:31] Precursor search
[0:36] Optimising weights
Averages: 
0.0840519 0.0729612 0 0.0380492 0.229215 0.0832416 
Weights: 
4.56601 0 0 0 1.88369 0.744442 
[0:36] Calculating q-values
[0:36] Number of IDs at 50%, 5%, 1%, 0.1% FDR: 1444, 534, 329, 0
[0:36] Calibrating retention times
[0:36] 329 precursors used for iRT estimation.
[0:36] Processing batches #8-9 out of 232 
[0:36] Precursor search
[0:40] Optimising weights
Averages: 
0.0816464 0.0685845 0 0.0323445 0.209672 0.091939 
Weights: 
4.80892 0 0 0 1.71498 0.627299 
[0:40] Calculating q-values
[0:40] Number of IDs at 50%, 5%, 1%, 0.1% FDR: 1888, 628, 405, 0
[0:40] Calibrating retention times
[0:40] 405 precursors used for iRT estimation.
[0:40] Processing batches #10-11 out of 232 
[0:40] Precursor search
[0:45] Optimising weights
Averages: 
0.0828344 0.0741447 0 0.034183 0.203002 0.0735426 
Weights: 
5.00485 0 0 0 1.60078 0.600954 
[0:45] Calculating q-values
[0:45] Number of IDs at 50%, 5%, 1%, 0.1% FDR: 2370, 787, 435, 0
[0:45] Calibrating retention times
[0:45] 435 precursors used for iRT estimation.
[0:45] Processing batches #12-14 out of 232 
[0:45] Precursor search
[0:52] Optimising weights
Averages: 
0.0800823 0.0717195 0 0.0351127 0.228498 0.0764668 
Weights: 
4.6002 0 0 0 1.89494 0.565051 
[0:52] Calculating q-values
[0:52] Number of IDs at 50%, 5%, 1%, 0.1% FDR: 3071, 988, 616, 0
[0:52] Calibrating retention times
[0:52] 616 precursors used for iRT estimation.
[0:52] Processing batches #15-17 out of 232 
[0:52] Precursor search
[0:59] Optimising weights
Averages: 
0.0801631 0.067005 0 0.0311519 0.208947 0.0861766 
Weights: 
4.80237 0 0 0 1.76458 0.53594 
[0:59] Calculating q-values
[0:59] Number of IDs at 50%, 5%, 1%, 0.1% FDR: 3743, 1249, 706, 0
[0:59] Calibrating retention times
[0:59] 706 precursors used for iRT estimation.
[0:59] Precursor search
[0:59] Optimising weights
Averages: 
0.0801631 0.067005 0 0.0311519 0.208947 0.0861766 
Weights: 
4.80237 0 0 0 1.76458 0.53594 
[0:59] Calculating q-values
[0:59] Number of IDs at 50%, 5%, 1%, 0.1% FDR: 3743, 1249, 706, 0
[0:59] Precursor search
[0:59] Optimising weights
Averages: 
0.0801631 0.067005 0 0.0311519 0.208947 0.0861766 
Weights: 
4.80237 0 0 0 1.76458 0.53594 
[0:59] Calculating q-values
[0:59] Number of IDs at 50%, 5%, 1%, 0.1% FDR: 3743, 1249, 706, 0
[0:59] Precursor search
[1:00] Optimising weights
Averages: 
0.0801631 0.067005 0 0.0311519 0.208947 0.0861766 
Weights: 
4.80237 0 0 0 1.76458 0.53594 
[1:00] Calculating q-values
[1:00] Number of IDs at 50%, 5%, 1%, 0.1% FDR: 3743, 1249, 706, 0
[1:00] Calibrating retention times
[1:00] 706 precursors used for iRT estimation.
[1:00] RT window set to 0.972632
[1:00] Ion mobility window set to 0.0162288
[1:00] Peak width: 5.316
[1:00] Scan window radius set to 11
[1:00] Mass correction transform (1243 precursors): -4.05684e-09 -0.00470156 1.17403e-05 
[1:00] M/z SD: 3.72114 ppm
[1:00] Top 70% mass accuracy: 4.29712 ppm
[1:00] Top 70% mass accuracy without correction: 5.07142ppm
[1:00] MS1 mass correction transform (1088 precursors): -4.19346e-09 -0.0075217 1.99197e-05 
[1:00] Top 70% MS1 mass accuracy: 4.37167 ppm
[1:00] Top 70% MS1 mass accuracy without correction: 5.53024ppm
[1:00] Refining mass correction
[1:00] Calibrating retention times
[1:00] Mass correction transform (870 precursors): -4.09025e-09 -0.00488256 1.18806e-05 
[1:00] M/z SD: 3.72831 ppm
[1:00] Top 70% mass accuracy: 4.32579 ppm
[1:00] Top 70% mass accuracy without correction: 5.07142ppm
[1:00] MS1 mass correction transform (761 precursors): -3.55946e-09 -0.00555748 1.50915e-05 
[1:00] Top 70% MS1 mass accuracy: 3.65147 ppm
[1:00] Top 70% MS1 mass accuracy without correction: 5.53024ppm
[1:00] Recommended MS1 mass accuracy setting: 18.2574 ppm
[1:00] Suggested mass accuracy: 12.9774 ppm
[1:00] Processing batch #1 out of 232 
[1:00] Precursor search
[1:00] Optimising weights
Averages: 
0.176565 0.0866876 0 0.0711743 0.279927 0.34732 
Weights: 
1e-09 0 0 0 3.48586 3.32565 
[1:00] Calculating q-values
[1:00] Number of IDs at 50%, 5%, 1%, 0.1% FDR: 189, 132, 111, 0
[1:00] Calibrating retention times
[1:00] 111 precursors used for iRT estimation.
[1:00] Processing batch #2 out of 232 
[1:00] Precursor search
[1:01] Optimising weights
Averages: 
0.253081 0.141886 0 0.113758 0.313802 0.349912 
Weights: 
0.577907 0 0 0 3.13532 2.38639 
[1:01] Calculating q-values
[1:01] Number of IDs at 50%, 5%, 1%, 0.1% FDR: 397, 274, 214, 0
[1:01] Calibrating retention times
[1:01] 214 precursors used for iRT estimation.
[1:01] Processing batch #3 out of 232 
[1:01] Precursor search
[1:01] Optimising weights
Averages: 
0.263061 0.148849 0 0.111014 0.312555 0.337728 
Weights: 
0.751838 0 0 0 2.94548 2.27024 
[1:01] Calculating q-values
[1:01] Number of IDs at 50%, 5%, 1%, 0.1% FDR: 581, 409, 319, 0
[1:01] Calculating q-values
[1:01] Number of IDs at 50%, 5%, 1%, 0.1% FDR: 581, 416, 331, 0
[1:01] Forcing weights reset
[1:01] Precursor search
[1:01] Optimising weights
Averages: 
0.252521 0.140753 0 0.112622 0.313844 0.349912 
Weights: 
0.680364 0 0 0 3.02478 2.54921 
[1:01] Calculating q-values
[1:01] Number of IDs at 50%, 5%, 1%, 0.1% FDR: 581, 406, 315, 0
[1:01] Calibrating retention times
[1:01] 315 precursors used for iRT estimation.
[1:01] Processing batch #4 out of 232 
[1:01] Precursor search
[1:01] Optimising weights
Averages: 
0.247825 0.12802 0 0.0951671 0.308113 0.338454 
Weights: 
1.32197 0 0 0 2.61617 2.00573 
[1:01] Calculating q-values
[1:02] Number of IDs at 50%, 5%, 1%, 0.1% FDR: 777, 549, 380, 0
[1:02] Calibrating retention times
[1:02] 380 precursors used for iRT estimation.
[1:02] Processing batch #5 out of 232 
[1:02] Precursor search
[1:02] Optimising weights
Averages: 
0.239767 0.120001 0 0.0899837 0.285321 0.350149 
Weights: 
1.40588 0 0 0 2.72123 1.86783 
[1:02] Calculating q-values
[1:02] Number of IDs at 50%, 5%, 1%, 0.1% FDR: 978, 685, 457, 0
[1:02] Calibrating retention times
[1:02] 457 precursors used for iRT estimation.
[1:02] Processing batches #6-7 out of 232 
[1:02] Precursor search
[1:02] Optimising weights
Averages: 
0.237774 0.12137 0 0.0964139 0.275979 0.306917 
Weights: 
1.75122 0 0 0 2.74248 1.60774 
[1:02] Calculating q-values
[1:02] Number of IDs at 50%, 5%, 1%, 0.1% FDR: 1403, 965, 707, 0
[1:02] Calibrating retention times
[1:02] 707 precursors used for iRT estimation.
[1:02] Processing batches #8-9 out of 232 
[1:02] Precursor search
[1:03] Optimising weights
Averages: 
0.241653 0.121532 0 0.0968082 0.266776 0.338437 
Weights: 
1.50131 0 0 0 2.89251 1.87911 
[1:03] Calculating q-values
[1:03] Number of IDs at 50%, 5%, 1%, 0.1% FDR: 1834, 1272, 911, 0
[1:03] Calibrating retention times
[1:03] 911 precursors used for iRT estimation.
[1:03] Processing batches #10-11 out of 232 
[1:03] Precursor search
[1:03] Optimising weights
Averages: 
0.252257 0.132799 0 0.105511 0.274075 0.334203 
Weights: 
1.64321 0 0 0 2.69978 1.83193 
[1:03] Calculating q-values
[1:04] Number of IDs at 50%, 5%, 1%, 0.1% FDR: 2231, 1561, 1035, 0
[1:04] Calibrating retention times
[1:04] 1035 precursors used for iRT estimation.
[1:04] Processing batches #12-14 out of 232 
[1:04] Precursor search
[1:04] Optimising weights
Averages: 
0.251921 0.138014 0 0.103442 0.27242 0.314477 
Weights: 
1.82955 0 0 0 2.61063 1.72957 
[1:04] Calculating q-values
[1:04] Number of IDs at 50%, 5%, 1%, 0.1% FDR: 2844, 1983, 1324, 0
[1:04] Calculating q-values
[1:04] Number of IDs at 50%, 5%, 1%, 0.1% FDR: 2844, 1997, 1384, 0
[1:04] Calibrating retention times
[1:04] 1384 precursors used for iRT estimation.
[1:04] Processing batches #15-17 out of 232 
[1:04] Precursor search
[1:05] Optimising weights
Averages: 
0.250479 0.136141 0 0.098242 0.257565 0.339921 
Weights: 
1.62294 0 0 0 2.63768 1.95922 
[1:05] Calculating q-values
[1:05] Number of IDs at 50%, 5%, 1%, 0.1% FDR: 3466, 2466, 1577, 0
[1:05] Calibrating retention times
[1:05] 1577 precursors used for iRT estimation.
[1:05] Precursor search
[1:05] Optimising weights
Averages: 
0.253401 0.138014 0 0.103402 0.27143 0.333635 0 0 0 0 |***| 0 0 0 0 0 0 0 0 0.197795 0.213388 |***| 1.02619 0.228566 0.937578 0.210692 -1.42766 0.251812 0.0991727 0.468256 0.149541 0.158769 |***| 0.161479 0.10602 0.110246 0 0.0606558 0.193885 0 0 0.357748 0.2855 |***| 0.472656 0.49279 0.298898 -0.0322485 -0.0192714 -0.0127768 
Weights: 
1e-09 0 0 0 0 0.483624 0 0 0 0 |***| 0 0 0 0 0 0 0 0 0 1.54609 |***| 0 1.05487 0 0.662492 0 0.0120588 0 0 0.336361 0.353236 |***| 0 0 0 0 0.216698 0 0 0 0.530276 0.0511228 |***| 0 0 0 -2.1176 -5.32789 -3.07761 
[1:05] Calculating q-values
[1:05] Number of IDs at 50%, 5%, 1%, 0.1% FDR: 3452, 2566, 2025, 1382
[1:05] Calibrating retention times
[1:05] 2025 precursors used for iRT estimation.
[1:05] Precursor search
[1:05] Optimising weights
Averages: 
0.251736 0.144515 0 0.106008 0.266991 0.328693 0 0 0 0 |***| 0 0 0 0 0 0 0 0 0.196954 0.213843 |***| 1.0176 0.225306 0.981657 0.206225 -1.38198 0.11391 0.112705 0.480823 0.145973 0.165544 |***| 0.165564 0.108975 0.108909 0 0.0601664 0.199934 0 0 0.352927 0.274952 |***| 0.469272 0.489844 0.306133 -0.0317674 -0.0195138 -0.0117486 
Weights: 
1e-09 0 0 0 0.19884 0.653397 0 0 0 0 |***| 0 0 0 0 0 0 0 0 0 1.66216 |***| 0 0.67242 0 0 0.0018673 0.00530249 0.597463 0 0.0479815 0.446016 |***| 0.442057 0 0 0 0.77466 0 0 0 0.509326 0 |***| 0 0 0 -2.84668 -1.77585 -3.34589 
[1:05] Calculating q-values
[1:05] Number of IDs at 50%, 5%, 1%, 0.1% FDR: 3456, 2570, 1937, 1247
[1:05] Calibrating retention times
[1:05] 1937 precursors used for iRT estimation.
[1:05] Precursor search
[1:05] Optimising weights
Averages: 
0.245919 0.138037 0 0.0996476 0.264847 0.331103 0 0 0 0 |***| 0 0 0 0 0 0 0 0 0.190735 0.210391 |***| 0.998214 0.221527 0.956364 0.202534 -1.34132 0.261992 0.107383 0.474075 0.144912 0.156112 |***| 0.160845 0.107697 0.107161 0 0.061229 0.194394 0 0 0.347157 0.276349 |***| 0.461402 0.481016 0.299953 -0.0287405 -0.0190144 -0.0128513 
Weights: 
1e-09 0 0 0 0.682455 0.7101 0 0 0 0 |***| 0 0 0 0 0 0 0 0 0 1.92871 |***| 0 0.566995 0 0 0.0289823 0.015291 0.0968504 0 0 0.329618 |***| 0.0907483 0 0 0 0.72604 0 0 0 0.369675 0 |***| 0 0 0 -1.76586 -3.47378 -4.21532 
[1:06] Calculating q-values
[1:06] Number of IDs at 50%, 5%, 1%, 0.1% FDR: 3462, 2552, 1979, 1343
[1:06] Trying the other linear classifier
[1:06] Precursor search
[1:06] Optimising weights
Averages: 
0.24702 0.141426 0 0.103146 0.26482 0.322954 0 0 0 0 |***| 0 0 0 0 0 0 0 0 0.193195 0.212039 |***| 1.00232 0.221553 0.96357 0.203573 -1.35514 0.0607705 0.113109 0.474485 0.143969 0.163651 |***| 0.161619 0.105858 0.107601 0 0.0612918 0.197838 0 0 0.344591 0.267911 |***| 0.468582 0.48364 0.300727 -0.0297511 -0.0196837 -0.012518 
Weights: 
1e-09 0 0 0 0 1.26896 0 0 0 0 |***| 0 0 0 0 0 0 0 0 0 0.235381 |***| 0 0 0 3.22567 0 0.0380128 0.0162674 0 0 0.126379 |***| 1.625 0 0 0 0 0 0 0 0.71257 0.00493005 |***| 0.054092 0 0 -3.68272 -8.98635 -4.69421 
[1:06] Calculating q-values
[1:06] Number of IDs at 50%, 5%, 1%, 0.1% FDR: 3462, 2616, 1970, 1089
[1:06] Calibrating retention times
[1:06] 1970 precursors used for iRT estimation.
[1:06] Precursor search
[1:06] Optimising weights
Averages: 
0.249151 0.138706 0 0.104555 0.271405 0.327874 0 0 0 0 |***| 0 0 0 0 0 0 0 0 0.194505 0.21288 |***| 1.01752 0.223223 0.934656 0.208915 -1.42784 0.00529606 0.109149 0.477699 0.14718 0.158453 |***| 0.162301 0.110121 0.109374 0 0.0566207 0.195872 0 0 0.355 0.276801 |***| 0.465149 0.478944 0.303819 -0.0303036 -0.0192683 -0.0130824 
Weights: 
1e-09 0 0 0 0 1.26977 0 0 0 0 |***| 0 0 0 0 0 0 0 0 0 0.130728 |***| 0 0 0 3.2462 0 0.0327495 0 0 0 0.119878 |***| 1.72147 0 0 0 0 0 0 0 0.721742 0.0335044 |***| 0.0600051 0 0 -3.54335 -8.71169 -4.58526 
[1:06] Calculating q-values
[1:06] Number of IDs at 50%, 5%, 1%, 0.1% FDR: 3459, 2414, 1619, 1065
[1:06] Calculating q-values
[1:06] Number of IDs at 50%, 5%, 1%, 0.1% FDR: 3459, 2619, 2002, 1116
[1:06] Calibrating retention times
[1:06] 2002 precursors used for iRT estimation.
[1:06] Restoring classifier and weights to
1e-09 0 0 0 0 1.26896 0 0 0 0 |***| 0 0 0 0 0 0 0 0 0 0.235381 |***| 0 0 0 3.22567 0 0.0380128 0.0162674 0 0 0.126379 |***| 1.625 0 0 0 0 0 0 0 0.71257 0.00493005 |***| 0.054092 0 0 -3.68272 -8.98635 -4.69421 
[1:06] Precursor search
[1:54] Optimising weights
Averages: 
0.267344 0.143288 0 0.104106 0.286394 0.306038 0 0 0 0 |***| 0 0 0 0 0 0 0 0 0.216338 0.221311 |***| 1.02775 0.224464 1.02095 0.217485 -1.45489 -0.00188826 0.110309 0.489437 0.132704 0.105051 |***| 0.17199 0.111827 0.117677 0.299681 0.0507643 0.202516 0.0660939 0.481597 0.344332 0.292304 |***| 0.500732 0.510477 0.320797 -0.0295543 -0.0175834 -0.007447 
Weights: 
0.474421 0 0 0 0 1.39029 0 0 0 0 |***| 0 0 0 0 0 0 0 0 0.151485 0 |***| 0 0 0 3.23552 0.000332677 0.0437914 0 0 0 0 |***| 1.31121 0 0 0 0 0 0 0.032555 0.524411 0.0277506 |***| 0.0387024 0 0 -6.01398 -4.54831 -6.6002 
[1:54] Calculating q-values
[1:54] Number of IDs at 50%, 5%, 1%, 0.1% FDR: 48368, 34569, 23511, 7208
[1:54] Calculating q-values
[1:54] Number of IDs at 50%, 5%, 1%, 0.1% FDR: 48368, 37088, 28250, 11242
[1:54] Removing low confidence identifications
[1:54] Removing interfering precursors
[1:56] Calculating q-values
[1:56] Number of IDs at 50%, 5%, 1%, 0.1% FDR: 40904, 32243, 25637, 10428
[1:56] Calibrating retention times
[1:56] 25637 precursors used for iRT estimation.
[1:57] Optimising weights
Averages: 
0.282373 0.150139 0 0.110147 0.305302 0.327923 0 0 0 0 |***| 0 0 0 0 0 0 0 0 0.227719 0.231637 |***| 1.07215 0.231201 1.00708 0.229544 -1.59216 -0.148435 0.116974 0.514851 0.141922 0.112843 |***| 0.17966 0.117256 0.121224 0.345174 0.0490666 0.214013 0.0678151 0.525335 0.391801 0.334172 |***| 0.540674 0.575861 0.368062 -0.0331759 -0.0192861 -0.00959589 
Weights: 
0.0856069 0 0 0 0 1.02866 0 0 0 0 |***| 0 0 0 0 0 0 0 0 0 0 |***| 0 0 0 3.87892 0 0.011923 0.097252 0 0 0 |***| 0.41391 0 0 0 0 0 0 0.0666182 1.31561 0.266884 |***| 0 0.0356493 0 -7.52254 -6.3125 -10.4305 
[1:57] Training neural networks: 40904 targets, 11913 decoys
[2:00] Calculating q-values
[2:00] Number of IDs at 50%, 5%, 1%, 0.1% FDR: 40904, 34215, 31669, 26370
[2:00] Calibrating retention times
[2:00] 31669 precursors used for iRT estimation.
[2:01] Calculating protein q-values
[2:01] Number of genes identified at 1% FDR: 4605 (precursor-level), 4187 (protein-level) (inference performed using proteotypic peptides only)
[2:01] Quantification
[2:02] Quantification information saved to ./REDACTED_H5_DIA_d.quant.

[2:02] Cross-run analysis
[2:02] Reading quantification information: 1 files
[2:02] Averaged recommended settings for this experiment: Mass accuracy = 13ppm, MS1 accuracy = 18ppm, Scan window = 11
[2:02] Quantifying peptides
[2:02] Assembling protein groups
[2:03] Quantifying proteins
[2:03] Calculating q-values for protein and gene groups
[2:03] Calculating global q-values for protein and gene groups
[2:03] Writing report
[2:04] Report saved to report.tsv.
[2:04] Stats report saved to report.stats.tsv
[2:04] Log saved to report.log.txt
Finished

summary_small1_diannsummary.log

DIA-NN 1.8.1 (Data-Independent Acquisition by Neural Networks)
Compiled on Apr 15 2022 08:45:18
Current date and time: Mon Aug 14 20:35:24 2023
Logical CPU cores: 16
Thread number set to 12
Scan windows will be inferred separately for different runs
A fast algorithm will be used to select the MS2 mass accuracy setting
Mass accuracy will be determined separately for different runs
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
Implicit protein grouping: genes; this determines which peptides are considered 'proteotypic' and thus affects protein FDR calculation
Existing .quant files will be used
Precursor/protein x samples expression level matrices will be saved along with the main report
Output will be filtered at 0.01 FDR
DIA-NN will optimise the mass accuracy separately for each 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.

4 files will be processed
[0:00] Loading spectral library empirical_library.tsv
[0:03] Spectral library loaded: 5288 protein isoforms, 5288 protein groups and 41283 precursors in 38942 elution groups.
[0:03] Loading protein annotations from FASTA uniprot_human_sp_isoforms_2022-03-15_crap.fasta
[0:03] Annotating library proteins with information from the FASTA database
[0:03] Protein names missing for some isoforms
[0:03] Gene names missing for some isoforms
[0:03] Library contains 5263 proteins, and 5256 genes
[0:03] Initialising library
[0:03] Saving the library to empirical_library.tsv.speclib

[0:03] Cross-run analysis
[0:03] Reading quantification information: 4 files
[0:04] Averaged recommended settings for this experiment: Mass accuracy = 13ppm, MS1 accuracy = 18ppm, Scan window = 11
[0:04] Quantifying peptides
[0:05] Assembling protein groups
[0:05] Quantifying proteins
[0:05] Calculating q-values for protein and gene groups
[0:06] Calculating global q-values for protein and gene groups
[0:06] Writing report
[0:14] Report saved to diann_report.tsv.
[0:14] Saving precursor levels matrix
[0:15] Precursor levels matrix (1% precursor and protein group FDR) saved to diann_report.pr_matrix.tsv.
[0:15] Saving protein group levels matrix
[0:15] Protein group levels matrix (1% precursor FDR and protein group FDR) saved to diann_report.pg_matrix.tsv.
[0:15] Saving gene group levels matrix
[0:15] Gene groups levels matrix (1% precursor FDR and protein group FDR) saved to diann_report.gg_matrix.tsv.
[0:15] Saving unique genes levels matrix
[0:15] Unique genes levels matrix (1% precursor FDR and protein group FDR) saved to diann_report.unique_genes_matrix.tsv.
[0:15] Stats report saved to diann_report.stats.tsv
[0:15] Log saved to diann_report.log.txt
Finished

summary_small2_diannsummary.log

DIA-NN 1.8.1 (Data-Independent Acquisition by Neural Networks)
Compiled on Apr 15 2022 08:45:18
Current date and time: Mon Aug 14 20:34:22 2023
Logical CPU cores: 16
Thread number set to 12
Scan windows will be inferred separately for different runs
A fast algorithm will be used to select the MS2 mass accuracy setting
Mass accuracy will be determined separately for different runs
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
Implicit protein grouping: genes; this determines which peptides are considered 'proteotypic' and thus affects protein FDR calculation
Existing .quant files will be used
Precursor/protein x samples expression level matrices will be saved along with the main report
Output will be filtered at 0.01 FDR
DIA-NN will optimise the mass accuracy separately for each 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.

4 files will be processed
[0:00] Loading spectral library empirical_library.tsv
[0:01] Spectral library loaded: 5288 protein isoforms, 5288 protein groups and 41283 precursors in 38942 elution groups.
[0:01] Loading protein annotations from FASTA uniprot_human_sp_isoforms_2022-03-15_crap.fasta
[0:02] Annotating library proteins with information from the FASTA database
[0:02] Protein names missing for some isoforms
[0:02] Gene names missing for some isoforms
[0:02] Library contains 5263 proteins, and 5256 genes
[0:02] Initialising library
[0:02] Saving the library to empirical_library.tsv.speclib

[0:02] Cross-run analysis
[0:02] Reading quantification information: 4 files
[0:02] Averaged recommended settings for this experiment: Mass accuracy = 13ppm, MS1 accuracy = 19ppm, Scan window = 11
[0:02] Quantifying peptides
[0:04] Assembling protein groups
[0:04] Quantifying proteins
[0:04] Calculating q-values for protein and gene groups
[0:04] Calculating global q-values for protein and gene groups
[0:04] Writing report
[0:11] Report saved to diann_report.tsv.
[0:11] Saving precursor levels matrix
[0:11] Precursor levels matrix (1% precursor and protein group FDR) saved to diann_report.pr_matrix.tsv.
[0:11] Saving protein group levels matrix
[0:11] Protein group levels matrix (1% precursor FDR and protein group FDR) saved to diann_report.pg_matrix.tsv.
[0:11] Saving gene group levels matrix
[0:11] Gene groups levels matrix (1% precursor FDR and protein group FDR) saved to diann_report.gg_matrix.tsv.
[0:11] Saving unique genes levels matrix
[0:11] Unique genes levels matrix (1% precursor FDR and protein group FDR) saved to diann_report.unique_genes_matrix.tsv.
[0:11] Stats report saved to diann_report.stats.tsv
[0:11] Log saved to diann_report.log.txt
Finished