DIA-NN 1.8.1 (Data-Independent Acquisition by Neural Networks)
Compiled on Apr 14 2022 15:31:19
Current date and time: Thu Oct 13 22:02:54 2022
CPU: GenuineIntel Intel(R) Core(TM) i9-9900 CPU @ 3.10GHz
SIMD instructions: AVX AVX2 FMA SSE4.1 SSE4.2 
Logical CPU cores: 16
diann.exe --f H:\JD\eJD1443.raw  --f H:\JD\eJD1445.raw  --f H:\JD\eJD1444.raw  --lib F:\JD\plexDIA\Bulk_plexDIA\100x_LibGen_wJD1197_1199.tsv --threads 8 --verbose 3 --out H:\JD\eJD1443_45\Report.tsv --qvalue 0.01 --matrices --out-lib H:\JD\eJD1443_45\Report-lib.tsv --gen-spec-lib --fasta F:\JD\plexDIA\swissprot_human_CanIso_02142022.fasta --met-excision --cut K*,R* --window 5 --mass-acc 10 --mass-acc-ms1 5 --reanalyse --relaxed-prot-inf --smart-profiling --fixed-mod mTRAQ, 140.0949630177, nK  --channels mTRAQ,0,nK,0:0; mTRAQ,4,nK,4.0070994:4.0070994;mTRAQ,8,nK,8.0141988132:8.0141988132  --peak-translation  --original-mods  --report-lib-info  --ms1-isotope-quant 

Thread number set to 8
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
N-terminal methionine excision enabled
In silico digest will involve cuts at K*,R*
Scan window radius set to 5
A spectral library will be created from the DIA runs and used to reanalyse them; .quant files will only be saved to disk during the first step
Highly heuristic protein grouping will be used, to reduce the number of protein groups obtained; this mode is recommended for benchmarking protein ID numbers; use with caution for anything else
When generating a spectral library, in silico predicted spectra will be retained if deemed more reliable than experimental ones
Modification mTRAQ with mass delta 140.095 at  nK will be considered as fixed
Translation of retention times between peptides within the same elution group enabled
DIA-NN will not attempt to convert library modifications to the UniMod format
First +13C isotopic peak will be used for MS1 quantification in addition to the monoisotopic peak
Mass accuracy will be fixed to 1e-05 (MS2) and 5e-06 (MS1)
Registered mTRAQ channel 0 with following masses: n[0] K[0] 
Registered mTRAQ channel 4 with following masses: n[4.0071] K[4.0071] 
Registered mTRAQ channel 8 with following masses: n[8.0142] K[8.0142] 
Registered mTRAQ channel decoy with following masses: n[12.0213] K[12.0213] 

3 files will be processed
[0:00] Loading spectral library F:\JD\plexDIA\Bulk_plexDIA\100x_LibGen_wJD1197_1199.tsv
[0:02] Spectral library loaded: 8069 protein isoforms, 4993 protein groups and 36661 precursors in 16960 elution groups.
[0:02] Loading protein annotations from FASTA F:\JD\plexDIA\swissprot_human_CanIso_02142022.fasta
[0:03] Annotating library proteins with information from the FASTA database
[0:03] Gene names missing for some isoforms
[0:03] Library contains 3974 proteins, and 3968 genes
[0:03] Splitting library entries across channels
[0:03] Assembling elution groups
[0:03] Initialising library
[0:03] Saving the library to F:\JD\plexDIA\Bulk_plexDIA\100x_LibGen_wJD1197_1199.tsv.speclib

[0:04] First pass: generating a spectral library from DIA data
[0:04] File #1/3
[0:04] Loading run H:\JD\eJD1443.raw
[0:07] Detected MS/MS range: 63.6681 - 2868.58
[0:08] Run loaded
[0:08] 146291 library precursors are potentially detectable
[0:08] Processing batch #1 out of 73 
[0:08] Precursor search
[0:08] Optimising weights
Averages: 
0.00694008 -0.021151 0 -0.000585124 0.00713954 0.0722684 
Weights: 
5.75595 0 0 0.476764 0.96658 0.549039 
[0:08] Calculating q-values
[0:08] Number of IDs at 50%, 5%, 1%, 0.1% FDR: 226, 40, 0, 0
[0:08] Calibrating retention times
[0:08] 50 precursors used for iRT estimation.
[0:08] Processing batch #2 out of 73 
[0:08] Precursor search
[0:09] Optimising weights
Averages: 
0.0311224 0.00920645 0 0.00364206 0.173489 0.105501 
Weights: 
3.77795 0.954948 0 2.48427 1.79336 1.33564 
[0:09] Calculating q-values
[0:09] Number of IDs at 50%, 5%, 1%, 0.1% FDR: 377, 90, 0, 0
[0:09] Calibrating retention times
[0:09] 50 precursors used for iRT estimation.
[0:09] Processing batch #3 out of 73 
[0:09] Precursor search
[0:10] Optimising weights
Averages: 
0.0129409 0.00898117 0 0.00861904 0.189559 0.0108648 
Weights: 
2.18293 0.52198 0 2.8727 2.22926 0.994315 
[0:10] Calculating q-values
[0:10] Number of IDs at 50%, 5%, 1%, 0.1% FDR: 531, 149, 116, 0
[0:10] Calibrating retention times
[0:10] 116 precursors used for iRT estimation.
[0:10] Precursor search
[0:10] Optimising weights
Averages: 
0.0120438 0.00286218 0 0.00174037 0.138641 0.0396709 
Weights: 
5.76211 0 0 1.83616 2.02698 1.03983 
[0:10] Calculating q-values
[0:10] Number of IDs at 50%, 5%, 1%, 0.1% FDR: 570, 133, 104, 0
[0:10] Calibrating retention times
[0:10] 104 precursors used for iRT estimation.
[0:10] Mass correction transform (133 precursors): 3.17238e-09 -0.00135781 -1.50568e-06 
[0:10] M/z SD: 17.5377 ppm
[0:10] Top 70% mass accuracy: 2.68281 ppm
[0:10] Top 70% mass accuracy without correction: 2.92455ppm
[0:10] MS1 mass correction transform (122 precursors): -1.58821e-10 -0.000344759 1.12828e-06 
[0:10] Top 70% MS1 mass accuracy: 1.92785 ppm
[0:10] Top 70% MS1 mass accuracy without correction: 2.13368ppm
[0:10] Recalibrating with mass accuracy 1.3414e-05, 9.63923e-06 (MS2, MS1)
[0:10] Processing batch #1 out of 73 
[0:10] Precursor search
[0:11] Optimising weights
Averages: 
0.0519598 0.0342598 0 0.0433488 0.082798 0.0981167 
Weights: 
0.857451 0 0 2.71618 0.896062 0.536587 
[0:11] Calculating q-values
[0:11] Number of IDs at 50%, 5%, 1%, 0.1% FDR: 545, 225, 197, 0
[0:11] Calibrating retention times
[0:11] 197 precursors used for iRT estimation.
[0:11] Processing batch #2 out of 73 
[0:11] Precursor search
[0:11] Optimising weights
Averages: 
0.0550448 0.0351879 0 0.0426076 0.0725006 0.0944631 
Weights: 
1.83647 0 0 2.5787 0.72066 0.396487 
[0:11] Calculating q-values
[0:11] Number of IDs at 50%, 5%, 1%, 0.1% FDR: 1083, 468, 361, 0
[0:11] Calibrating retention times
[0:11] 361 precursors used for iRT estimation.
[0:11] Processing batch #3 out of 73 
[0:11] Precursor search
[0:12] Optimising weights
Averages: 
0.0505221 0.0313891 0 0.0386703 0.0744073 0.103905 
Weights: 
1.06169 0 0 2.83062 0.868173 0.579944 
[0:12] Calculating q-values
[0:12] Number of IDs at 50%, 5%, 1%, 0.1% FDR: 1654, 684, 515, 0
[0:12] Calibrating retention times
[0:12] 515 precursors used for iRT estimation.
[0:12] Processing batch #4 out of 73 
[0:12] Precursor search
[0:13] Optimising weights
Averages: 
0.0515132 0.0321304 0 0.0389056 0.0738448 0.0991405 
Weights: 
1.39749 0 0 2.44754 0.856694 0.51728 
[0:13] Calculating q-values
[0:13] Number of IDs at 50%, 5%, 1%, 0.1% FDR: 2161, 908, 683, 0
[0:13] Calibrating retention times
[0:13] 683 precursors used for iRT estimation.
[0:13] Processing batch #5 out of 73 
[0:13] Precursor search
[0:14] Optimising weights
Averages: 
0.0503102 0.0309337 0 0.038135 0.072279 0.101729 
Weights: 
1.22718 0 0 2.5762 0.845391 0.581459 
[0:14] Calculating q-values
[0:14] Number of IDs at 50%, 5%, 1%, 0.1% FDR: 2676, 1102, 803, 0
[0:14] Calibrating retention times
[0:14] 803 precursors used for iRT estimation.
[0:14] Precursor search
[0:14] Optimising weights
Averages: 
0.050574 0.0312045 0 0.0386954 0.0721609 0.0986843 
Weights: 
1.29061 0 0 2.5784 0.819663 0.567378 
[0:14] Calculating q-values
[0:14] Number of IDs at 50%, 5%, 1%, 0.1% FDR: 2679, 1108, 800, 0
[0:14] Precursor search
[0:14] Optimising weights
Averages: 
0.0502839 0.0312437 0 0.0386381 0.0728567 0.0985598 
Weights: 
1.2076 0 0 2.62882 0.840063 0.5752 
[0:14] Calculating q-values
[0:14] Number of IDs at 50%, 5%, 1%, 0.1% FDR: 2678, 1103, 804, 0
[0:14] Precursor search
[0:14] Optimising weights
Averages: 
0.0505505 0.0311401 0 0.0384513 0.072567 0.0992527 
Weights: 
1.31407 0 0 2.534 0.834835 0.561614 
[0:14] Calculating q-values
[0:14] Number of IDs at 50%, 5%, 1%, 0.1% FDR: 2684, 1105, 803, 0
[0:14] Calibrating retention times
[0:14] 803 precursors used for iRT estimation.
[0:14] RT window set to 1.65443
[0:14] Mass correction transform (1095 precursors): 1.53732e-10 -0.000929514 7.10767e-07 
[0:14] M/z SD: 2.86225 ppm
[0:14] Top 70% mass accuracy: 3.51913 ppm
[0:14] Top 70% mass accuracy without correction: 3.612ppm
[0:14] MS1 mass correction transform (898 precursors): -2.42369e-09 -0.00235322 6.10425e-06 
[0:14] Top 70% MS1 mass accuracy: 2.04462 ppm
[0:14] Top 70% MS1 mass accuracy without correction: 2.14043ppm
[0:14] Refining mass correction
[0:14] Calibrating retention times
[0:14] Mass correction transform (766 precursors): -3.85391e-10 -0.0010572 1.21087e-06 
[0:14] M/z SD: 2.86921 ppm
[0:14] Top 70% mass accuracy: 3.5705 ppm
[0:14] Top 70% mass accuracy without correction: 3.612ppm
[0:14] MS1 mass correction transform (628 precursors): -1.9091e-09 -0.00203573 5.20691e-06 
[0:14] Top 70% MS1 mass accuracy: 2.06031 ppm
[0:14] Top 70% MS1 mass accuracy without correction: 2.14043ppm
[0:14] Recommended MS1 mass accuracy setting: 10.3015 ppm
[0:14] Using mass accuracy 1e-05, 5e-06 (MS2, MS1)
[0:14] Processing batch #1 out of 73 
[0:14] Precursor search
[0:14] Optimising weights
Averages: 
0.111404 0.0657931 0 0.0577564 0.0881627 0.192742 
Weights: 
2.01788 0.177477 0 1.84173 0.34981 0.783699 
[0:14] Calculating q-values
[0:14] Number of IDs at 50%, 5%, 1%, 0.1% FDR: 734, 371, 244, 0
[0:14] Calibrating retention times
[0:14] 244 precursors used for iRT estimation.
[0:14] Processing batch #2 out of 73 
[0:14] Precursor search
[0:14] Optimising weights
Averages: 
0.108062 0.0697547 0 0.0639495 0.100662 0.159623 
Weights: 
1.70192 0.58221 0 2.23515 0.551819 0.599508 
[0:14] Calculating q-values
[0:14] Number of IDs at 50%, 5%, 1%, 0.1% FDR: 1393, 685, 485, 0
[0:14] Calibrating retention times
[0:14] 485 precursors used for iRT estimation.
[0:14] Processing batch #3 out of 73 
[0:14] Precursor search
[0:14] Optimising weights
Averages: 
0.105857 0.0668 0 0.0616561 0.102532 0.163522 
Weights: 
1.72814 0.36397 0 2.28704 0.709577 0.605217 
[0:14] Calculating q-values
[0:14] Number of IDs at 50%, 5%, 1%, 0.1% FDR: 2087, 1071, 740, 0
[0:14] Calibrating retention times
[0:14] 740 precursors used for iRT estimation.
[0:14] Processing batch #4 out of 73 
[0:14] Precursor search
[0:14] Optimising weights
Averages: 
0.105656 0.0657841 0 0.0614378 0.0997773 0.162163 
Weights: 
1.85234 0 0 2.38965 0.716008 0.581424 
[0:14] Calculating q-values
[0:14] Number of IDs at 50%, 5%, 1%, 0.1% FDR: 2711, 1389, 1041, 0
[0:14] Calibrating retention times
[0:14] 1041 precursors used for iRT estimation.
[0:14] Processing batch #5 out of 73 
[0:14] Precursor search
[0:14] Optimising weights
Averages: 
0.102426 0.064505 0 0.0604839 0.0977557 0.160703 
Weights: 
1.55577 0.00501244 0 2.76171 0.725211 0.654532 
[0:15] Calculating q-values
[0:15] Number of IDs at 50%, 5%, 1%, 0.1% FDR: 3363, 1661, 1206, 0
[0:15] Calibrating retention times
[0:15] 1206 precursors used for iRT estimation.
[0:15] Precursor search
[0:15] Optimising weights
Averages: 
0.103345 0.0641106 0 0.0598241 0.0984743 0.164356 0 0 0 0 |***| 0 0 0 0 0 0 0 0 0.0759521 0.0842579 |***| 0.34032 0.0229172 0.372416 0.0754998 -0.889895 0.0920343 0.0686373 0.197895 0.156758 0 |***| 0.0563669 0.0372372 0.0297343 0 0.00446871 0.0864727 0 0 0.16083 0.108999 |***| 0.228666 0.17449 0.0977506 -0.0182838 0 -0.000908948 
Weights: 
1e-09 0 0 1.13656 0.277283 0.0403401 0 0 0 0 |***| 0 0 0 0 0 0 0 0 0 0.431903 |***| 0 0 0 0 0 0.00370189 0 0.0378987 0.241339 0 |***| 0.0725037 0 0 0 0 0.240647 0 0 0.318599 0 |***| 0.199783 0.052366 0.0444674 -1.45695 0 -0.646756 
[0:15] Calculating q-values
[0:15] Number of IDs at 50%, 5%, 1%, 0.1% FDR: 3448, 1683, 1215, 0
[0:15] Calibrating retention times
[0:15] 1215 precursors used for iRT estimation.
[0:15] Precursor search
[0:15] Optimising weights
Averages: 
0.107293 0.0675384 0 0.0637966 0.106422 0.159114 0 0 0 0 |***| 0 0 0 0 0 0 0 0 0.0807737 0.0880432 |***| 0.366199 0.0250964 0.410071 0.0846765 -0.987697 0.0666024 0.0696258 0.199689 0.145814 0 |***| 0.0596339 0.0399387 0.0314855 0 0.00852648 0.0903491 0 0 0.144825 0.0980786 |***| 0.213454 0.163373 0.0911825 -0.0119521 0 0.000918572 
Weights: 
1e-09 0 0 1.02391 0.349186 0 0 0 0 0 |***| 0 0 0 0 0 0 0 0 0 0.447251 |***| 0 0 0 0 0 0.00444196 0.104977 0.000124837 0.317409 0 |***| 0.210309 0 0 0 0 0.305179 0 0 0.310685 0 |***| 0.206715 0.0354642 0.0475004 -1.63315 0 -0.910726 
[0:15] Calculating q-values
[0:15] Number of IDs at 50%, 5%, 1%, 0.1% FDR: 3436, 1694, 1237, 1043
[0:15] Calibrating retention times
[0:15] 1237 precursors used for iRT estimation.
[0:15] Precursor search
[0:15] Optimising weights
Averages: 
0.107898 0.0681671 0 0.0646452 0.107163 0.159959 0 0 0 0 |***| 0 0 0 0 0 0 0 0 0.0812379 0.0882313 |***| 0.36787 0.0246943 0.409268 0.0856612 -1.00541 0.0575253 0.0700895 0.203178 0.14567 0 |***| 0.0587231 0.0394281 0.0313449 0 0.00792333 0.0906629 0 0 0.145016 0.0960333 |***| 0.213839 0.161933 0.090916 -0.0117681 0 0.00164334 
Weights: 
1e-09 0 0 1.05537 0.380645 0 0 0 0 0 |***| 0 0 0 0 0 0 0 0 0 0.383277 |***| 0 0 0 0 0 0.00545009 0.0962139 0.0119439 0.336491 0 |***| 0.132446 0 0 0 0 0.344703 0 0 0.294859 0 |***| 0.217475 0.0052937 0.0689412 -1.67041 0 -0.838907 
[0:15] Calculating q-values
[0:15] Number of IDs at 50%, 5%, 1%, 0.1% FDR: 3431, 1713, 1247, 1034
[0:15] Calibrating retention times
[0:15] 1247 precursors used for iRT estimation.
[0:15] Precursor search
[0:15] Optimising weights
Averages: 
0.108125 0.0683098 0 0.0644461 0.107498 0.159254 0 0 0 0 |***| 0 0 0 0 0 0 0 0 0.0815827 0.0886742 |***| 0.368762 0.025245 0.411271 0.0857495 -0.999818 0.0675576 0.0697583 0.202071 0.144487 0 |***| 0.0591448 0.0393623 0.0315045 0 0.00848413 0.0912103 0 0 0.145422 0.0970791 |***| 0.213755 0.1625 0.0914556 -0.0117168 0 0.00129294 
Weights: 
1e-09 0 0 0.980956 0.394986 0 0 0 0 0 |***| 0 0 0 0 0 0 0 0 0 0.345499 |***| 0 0 0 0 0 0.00632047 0.093886 0.0094483 0.32485 0 |***| 0.142672 0 0 0 0 0.424448 0 0 0.294415 0 |***| 0.21845 0.00466428 0.0782643 -1.68064 0 -0.913533 
[0:15] Calculating q-values
[0:15] Number of IDs at 50%, 5%, 1%, 0.1% FDR: 3232, 1434, 987, 0
[0:15] Calculating q-values
[0:15] Number of IDs at 50%, 5%, 1%, 0.1% FDR: 3447, 1716, 1231, 1037
[0:15] Calibrating retention times
[0:15] 1231 precursors used for iRT estimation.
[0:15] Restoring classifier and weights to
1e-09 0 0 1.05537 0.380645 0 0 0 0 0 |***| 0 0 0 0 0 0 0 0 0 0.383277 |***| 0 0 0 0 0 0.00545009 0.0962139 0.0119439 0.336491 0 |***| 0.132446 0 0 0 0 0.344703 0 0 0.294859 0 |***| 0.217475 0.0052937 0.0689412 -1.67041 0 -0.838907 
[0:15] Precursor search
[0:20] Optimising weights
Averages: 
0.108896 0.0688207 0 0.0654307 0.106699 0.160654 0 0 0 0 |***| 0 0 0 0 0 0 0 0 0.0821201 0.0892615 |***| 0.374672 0.024997 0.410409 0.0852828 -0.998396 0.0888282 0.0776132 0.220966 0.144827 0 |***| 0.0596337 0.0393223 0.0314164 0.0930367 0.00910371 0.091435 0.0299558 0.217018 0.144045 0.10018 |***| 0.213816 0.165325 0.0921192 -0.011778 0 0.00243819 
Weights: 
0.042893 0 0 0.839545 0.392157 0.0953332 0 0 0 0 |***| 0 0 0 0 0 0 0 0 0.132103 0.341545 |***| 0 0 0 0 0 0.0128044 0.192634 0.00339338 0.28904 0 |***| 0.173387 0 0 0.099926 0 0.388227 0 0 0.108415 0.0247692 |***| 0.166243 0.0703053 0.0524641 -1.54706 0 -0.864213 
[0:20] Calculating q-values
[0:20] Number of IDs at 50%, 5%, 1%, 0.1% FDR: 48898, 21332, 15183, 8346
[0:20] Calculating q-values
[0:20] Number of IDs at 50%, 5%, 1%, 0.1% FDR: 50839, 26310, 18583, 10395
[0:20] Removing low confidence identifications
[0:20] Removing interfering precursors
[0:22] Calculating q-values
[0:23] Number of IDs at 50%, 5%, 1%, 0.1% FDR: 38330, 24362, 17905, 10261
[0:23] Calibrating retention times
[0:23] 17905 precursors used for iRT estimation.
[0:23] Optimising weights
Averages: 
0.15563 0.096853 0 0.0922978 0.156075 0.244832 0 0 0 0 |***| 0 0 0 0 0 0 0 0 0.114824 0.125939 |***| 0.521426 0.0329708 0.547429 0.122487 -1.43215 0.101378 0.112342 0.314927 0.219369 0 |***| 0.0836112 0.0550769 0.0442124 0.139804 0.00993956 0.129314 0.0433145 0.310969 0.215856 0.149182 |***| 0.315648 0.244273 0.135828 -0.0162346 0 0.00225547 
Weights: 
1e-09 0 0 0.961754 0.695963 0.245947 0 0 0 0 |***| 0 0 0 0 0 0 0 0 0 0.248636 |***| 0 0 0 0 0 0.0216375 0.368144 0 0.481194 0 |***| 0 0 0 0.156344 0 0.639593 0 0 0.0957787 0.0157728 |***| 0.17123 0.0729312 0.0519542 -2.26271 0 -1.55755 
[0:23] Training neural networks: 25338 targets, 35671 decoys
[0:26] Calculating q-values
[0:26] Number of IDs at 50%, 5%, 1%, 0.1% FDR: 42982, 28235, 22637, 13838
[0:26] Calibrating retention times
[0:26] 22637 precursors used for iRT estimation.
[0:27] Translating peaks within elution groups
[0:27] Calculating q-values
[0:27] Number of IDs at 50%, 5%, 1%, 0.1% FDR: 63562, 51108, 44679, 31861
[0:27] Scoring channels
[0:28] Precursors at 1% channel FDR: 28051 out of 39030
[0:28] Calculating protein q-values
[0:28] Number of genes identified at 1% FDR: 1928 (precursor-level), 1538 (protein-level) (inference performed using proteotypic peptides only)
[0:28] Quantification
[0:30] Quantification information saved to H:\JD\eJD1443.raw.quant.

[0:30] File #2/3
[0:30] Loading run H:\JD\eJD1445.raw
[0:52] Detected MS/MS range: 63.6705 - 2872.88
[0:52] Run loaded
[0:52] 146291 library precursors are potentially detectable
[0:52] Processing batch #1 out of 73 
[0:52] Precursor search
[0:53] Optimising weights
Averages: 
0.0397855 0.0277282 0 0.0333417 0.0614108 0.0735907 
Weights: 
1.31163 0.518038 0 1.8742 0.52104 0.487153 
[0:53] Calculating q-values
[0:53] Number of IDs at 50%, 5%, 1%, 0.1% FDR: 454, 194, 115, 0
[0:53] Calibrating retention times
[0:53] 115 precursors used for iRT estimation.
[0:53] Precursor search
[0:53] Optimising weights
Averages: 
0.0404893 0.0290537 0 0.0341983 0.0605449 0.0722146 
Weights: 
0.53495 0.875795 0 2.48473 0.577587 0.659445 
[0:53] Calculating q-values
[0:53] Number of IDs at 50%, 5%, 1%, 0.1% FDR: 477, 183, 134, 0
[0:53] Calibrating retention times
[0:53] 134 precursors used for iRT estimation.
[0:53] Mass correction transform (182 precursors): -1.57464e-09 -0.00183743 3.44287e-06 
[0:53] M/z SD: 3.19828 ppm
[0:53] Top 70% mass accuracy: 3.53353 ppm
[0:53] Top 70% mass accuracy without correction: 3.61371ppm
[0:53] MS1 mass correction transform (141 precursors): -5.7596e-09 -0.00388573 1.08138e-05 
[0:53] Top 70% MS1 mass accuracy: 2.3629 ppm
[0:53] Top 70% MS1 mass accuracy without correction: 2.07535ppm
[0:53] No MS1 mass correction required
[0:53] Recalibrating with mass accuracy 1.76677e-05, 1.03768e-05 (MS2, MS1)
[0:53] Processing batch #1 out of 73 
[0:53] Precursor search
[0:54] Optimising weights
Averages: 
0.0468184 0.0326436 0 0.0368884 0.0674898 0.106226 
Weights: 
0.431901 1.76974 0 2.0885 0.439595 0.779701 
[0:54] Calculating q-values
[0:54] Number of IDs at 50%, 5%, 1%, 0.1% FDR: 552, 206, 0, 0
[0:54] Calibrating retention times
[0:54] 50 precursors used for iRT estimation.
[0:54] Processing batch #2 out of 73 
[0:54] Precursor search
[0:55] Optimising weights
Averages: 
0.0455334 0.0328488 0 0.0384621 0.071748 0.095451 
Weights: 
0.0693306 1.36727 0 2.70952 0.640836 0.770975 
[0:55] Calculating q-values
[0:55] Number of IDs at 50%, 5%, 1%, 0.1% FDR: 1070, 455, 318, 0
[0:55] Calibrating retention times
[0:55] 318 precursors used for iRT estimation.
[0:55] Processing batch #3 out of 73 
[0:55] Precursor search
[0:55] Optimising weights
Averages: 
0.0459123 0.0301829 0 0.0359118 0.0721013 0.0849003 
Weights: 
0.677197 0.884283 0 2.43766 0.860049 0.668182 
[0:55] Calculating q-values
[0:55] Number of IDs at 50%, 5%, 1%, 0.1% FDR: 1560, 658, 423, 0
[0:55] Calibrating retention times
[0:55] 423 precursors used for iRT estimation.
[0:55] Processing batch #4 out of 73 
[0:55] Precursor search
[0:56] Optimising weights
Averages: 
0.0467207 0.0307927 0 0.0357495 0.0684718 0.0909864 
Weights: 
0.913228 0.972538 0 2.19629 0.813214 0.663325 
[0:56] Calculating q-values
[0:56] Number of IDs at 50%, 5%, 1%, 0.1% FDR: 2151, 853, 530, 0
[0:56] Calibrating retention times
[0:56] 530 precursors used for iRT estimation.
[0:56] Processing batch #5 out of 73 
[0:56] Precursor search
[0:57] Optimising weights
Averages: 
0.0457862 0.0297965 0 0.0351516 0.068395 0.0863615 
Weights: 
1.24016 0.61696 0 2.12326 0.850458 0.603682 
[0:57] Calculating q-values
[0:57] Number of IDs at 50%, 5%, 1%, 0.1% FDR: 2594, 1010, 611, 0
[0:57] Calibrating retention times
[0:57] 611 precursors used for iRT estimation.
[0:57] Precursor search
[0:57] Optimising weights
Averages: 
0.0455462 0.0299287 0 0.0347489 0.0685509 0.0877681 
Weights: 
1.30306 0.694738 0 1.94466 0.871754 0.60085 
[0:57] Calculating q-values
[0:57] Number of IDs at 50%, 5%, 1%, 0.1% FDR: 2645, 1010, 629, 0
[0:57] Precursor search
[0:57] Optimising weights
Averages: 
0.0454877 0.029727 0 0.034831 0.0685644 0.0878401 
Weights: 
1.32568 0.6134 0 1.96548 0.879754 0.602842 
[0:57] Calculating q-values
[0:57] Number of IDs at 50%, 5%, 1%, 0.1% FDR: 2640, 1011, 640, 0
[0:57] Precursor search
[0:57] Optimising weights
Averages: 
0.0456076 0.0300626 0 0.0349844 0.068717 0.0864873 
Weights: 
1.40685 0.687079 0 1.85647 0.874757 0.582811 
[0:57] Calculating q-values
[0:57] Number of IDs at 50%, 5%, 1%, 0.1% FDR: 2644, 1011, 632, 0
[0:57] Calibrating retention times
[0:57] 632 precursors used for iRT estimation.
[0:57] RT window set to 1.69904
[0:57] Mass correction transform (1004 precursors): 1.07718e-09 -0.000907878 3.18691e-07 
[0:57] M/z SD: 3.227 ppm
[0:57] Top 70% mass accuracy: 3.78446 ppm
[0:57] Top 70% mass accuracy without correction: 3.95309ppm
[0:57] MS1 mass correction transform (816 precursors): -6.12311e-09 -0.00379966 1.1086e-05 
[0:57] Top 70% MS1 mass accuracy: 2.32364 ppm
[0:57] Top 70% MS1 mass accuracy without correction: 2.44608ppm
[0:57] Refining mass correction
[0:57] Calibrating retention times
[0:57] Mass correction transform (702 precursors): 3.961e-10 -0.000966466 6.68317e-07 
[0:57] M/z SD: 3.24584 ppm
[0:57] Top 70% mass accuracy: 3.80012 ppm
[0:57] Top 70% mass accuracy without correction: 3.95309ppm
[0:57] MS1 mass correction transform (571 precursors): -4.75883e-09 -0.00312069 8.94436e-06 
[0:57] Top 70% MS1 mass accuracy: 2.29902 ppm
[0:57] Top 70% MS1 mass accuracy without correction: 2.44608ppm
[0:57] Recommended MS1 mass accuracy setting: 11.4951 ppm
[0:57] Using mass accuracy 1e-05, 5e-06 (MS2, MS1)
[0:57] Processing batch #1 out of 73 
[0:57] Precursor search
[0:57] Optimising weights
Averages: 
0.110187 0.0655116 0 0.0607425 0.0998184 0.181214 
Weights: 
2.18709 0.205633 0 1.63004 0.484345 0.70526 
[0:57] Calculating q-values
[0:57] Number of IDs at 50%, 5%, 1%, 0.1% FDR: 710, 317, 209, 0
[0:57] Calibrating retention times
[0:57] 209 precursors used for iRT estimation.
[0:57] Processing batch #2 out of 73 
[0:57] Precursor search
[0:57] Optimising weights
Averages: 
0.107324 0.0661993 0 0.0649262 0.105056 0.171673 
Weights: 
1.48263 0.0942562 0 2.73868 0.637142 0.763008 
[0:57] Calculating q-values
[0:57] Number of IDs at 50%, 5%, 1%, 0.1% FDR: 1410, 668, 451, 0
[0:57] Calibrating retention times
[0:57] 451 precursors used for iRT estimation.
[0:57] Processing batch #3 out of 73 
[0:57] Precursor search
[0:57] Optimising weights
Averages: 
0.107037 0.0650277 0 0.061497 0.102296 0.17031 
Weights: 
1.82119 0.166055 0 2.15785 0.714247 0.690829 
[0:57] Calculating q-values
[0:57] Number of IDs at 50%, 5%, 1%, 0.1% FDR: 2052, 1005, 638, 0
[0:57] Calibrating retention times
[0:57] 638 precursors used for iRT estimation.
[0:57] Processing batch #4 out of 73 
[0:57] Precursor search
[0:58] Optimising weights
Averages: 
0.105514 0.064989 0 0.0620818 0.0995672 0.170422 
Weights: 
1.53372 0.100373 0 2.56749 0.668209 0.741241 
[0:58] Calculating q-values
[0:58] Number of IDs at 50%, 5%, 1%, 0.1% FDR: 2697, 1334, 908, 0
[0:58] Calibrating retention times
[0:58] 908 precursors used for iRT estimation.
[0:58] Processing batch #5 out of 73 
[0:58] Precursor search
[0:58] Optimising weights
Averages: 
0.103387 0.0629756 0 0.0599257 0.0978662 0.166772 
Weights: 
1.80155 0 0 2.31249 0.712146 0.671625 
[0:58] Calculating q-values
[0:58] Number of IDs at 50%, 5%, 1%, 0.1% FDR: 3354, 1590, 1061, 0
[0:58] Calibrating retention times
[0:58] 1061 precursors used for iRT estimation.
[0:58] Precursor search
[0:58] Optimising weights
Averages: 
0.103323 0.0637996 0 0.0608816 0.0979403 0.166495 0 0 0 0 |***| 0 0 0 0 0 0 0 0 0.0755737 0.0847385 |***| 0.345346 0.0219137 0.364213 0.0747504 -0.896121 0.162115 0.0724669 0.198805 0.156562 0 |***| 0.0564427 0.0374225 0.0294125 0 0.00398517 0.087939 0 0 0.161833 0.118613 |***| 0.229469 0.169613 0.0954068 -0.0203409 0 -0.000635016 
Weights: 
0.0735514 0 0 0.688513 0.312301 0.288986 0 0 0 0 |***| 0 0 0 0 0 0 0 0 0 0 |***| 0.0185927 0 0 0 0 0.0161496 0.0952443 0 0.0547949 0 |***| 0 0 0 0 0 0.807175 0 0 0.091768 0.156136 |***| 0.239724 0 0.0771762 -1.6367 0 -0.740738 
[0:58] Calculating q-values
[0:58] Number of IDs at 50%, 5%, 1%, 0.1% FDR: 3470, 1620, 1213, 0
[0:58] Calibrating retention times
[0:58] 1213 precursors used for iRT estimation.
[0:58] Precursor search
[0:58] Optimising weights
Averages: 
0.106873 0.0668608 0 0.0659487 0.105965 0.163555 0 0 0 0 |***| 0 0 0 0 0 0 0 0 0.079614 0.0870516 |***| 0.363605 0.0236003 0.385324 0.0836966 -0.982431 0.0680754 0.0738273 0.204124 0.151735 0 |***| 0.058963 0.0383661 0.0307577 0 0.00668495 0.0888099 0 0 0.153388 0.10878 |***| 0.217984 0.163067 0.0932637 -0.0117634 0 0.00132134 
Weights: 
1e-09 0 0 1.11909 0.396327 0.239788 0 0 0 0 |***| 0 0 0 0 0 0 0 0 0 0 |***| 0.00475003 0 0 0 0 0.0101918 0.132154 0 0.134938 0 |***| 0 0 0 0 0 0.628417 0 0 0.123984 0.125528 |***| 0.235802 0 0.122707 -1.6894 0 -1.11798 
[0:58] Calculating q-values
[0:58] Number of IDs at 50%, 5%, 1%, 0.1% FDR: 3437, 1597, 1231, 0
[0:58] Trying the other linear classifier
[0:58] Precursor search
[0:58] Optimising weights
Averages: 
0.103204 0.0637372 0 0.0608356 0.0980815 0.166487 0 0 0 0 |***| 0 0 0 0 0 0 0 0 0.075456 0.0847112 |***| 0.345027 0.0216047 0.360983 0.0750347 -0.903642 0.158091 0.0721454 0.198076 0.156329 0 |***| 0.0561291 0.0371425 0.0293618 0 0.00379821 0.0879885 0 0 0.162307 0.118363 |***| 0.228271 0.168664 0.0953224 -0.0198767 0 -0.000628906 
Weights: 
0.369489 0 0 1.993 0.546429 0.368967 0 0 0 0 |***| 0 0 0 0 0 0 0 0 0.577785 0 |***| 0 0 0 0 0 0 0.106117 0.0445326 0 0 |***| 0 0 0 0 0 0.523857 0 0 0.214775 0.196422 |***| 0.341867 0 0.0267393 -2.80459 0 -1.72348 
[0:58] Calculating q-values
[0:58] Number of IDs at 50%, 5%, 1%, 0.1% FDR: 3424, 1707, 1288, 1094
[0:58] Calibrating retention times
[0:58] 1288 precursors used for iRT estimation.
[0:58] Precursor search
[0:58] Optimising weights
Averages: 
0.105268 0.0638228 0 0.0620842 0.10433 0.177319 0 0 0 0 |***| 0 0 0 0 0 0 0 0 0.0757145 0.086033 |***| 0.341753 0.0224592 0.356179 0.0812115 -0.945717 0.167057 0.0721113 0.196044 0.167315 0 |***| 0.0562487 0.0377915 0.0290206 0 0.00455018 0.0879996 0 0 0.162392 0.115322 |***| 0.221668 0.165548 0.0948316 -0.0112297 0 0.000926481 
Weights: 
0.265181 0 0 2.3672 0.718621 0.490368 0 0 0 0 |***| 0 0 0 0 0 0 0 0 0.526697 0 |***| 0 0 0 0 0 0 0.168125 0.0481992 0 0 |***| 0 0 0 0 0 0.403693 0 0 0.241782 0.102706 |***| 0.251419 0 0.0803589 -2.31002 0 -2.42524 
[0:58] Calculating q-values
[0:58] Number of IDs at 50%, 5%, 1%, 0.1% FDR: 3489, 1750, 1303, 1029
[0:58] Calibrating retention times
[0:58] 1303 precursors used for iRT estimation.
[0:58] Precursor search
[0:58] Optimising weights
Averages: 
0.10618 0.0650915 0 0.0625626 0.102627 0.173155 0 0 0 0 |***| 0 0 0 0 0 0 0 0 0.0773207 0.0861249 |***| 0.353901 0.0229744 0.372889 0.0798271 -0.936727 0.131677 0.0701801 0.195333 0.163799 0 |***| 0.0574872 0.0384317 0.0299794 0 0.00556574 0.0882745 0 0 0.161308 0.115428 |***| 0.22382 0.167773 0.0959638 -0.0126646 0 0.0024486 
Weights: 
0.463149 0 0 2.41912 0.691145 0.420821 0 0 0 0 |***| 0 0 0 0 0 0 0 0 0.550293 0 |***| 0 0 0 0 0 0 0.0904334 0.0666012 0 0 |***| 0 0 0 0 0 0.211655 0 0 0.256683 0.110854 |***| 0.264149 0 0.101872 -2.32011 0 -2.19424 
[0:58] Calculating q-values
[0:58] Number of IDs at 50%, 5%, 1%, 0.1% FDR: 3136, 1430, 767, 0
[0:58] Calculating q-values
[0:58] Number of IDs at 50%, 5%, 1%, 0.1% FDR: 3500, 1747, 1306, 1027
[0:58] Trying the other linear classifier
[0:58] Precursor search
[0:58] Optimising weights
Averages: 
0.10571 0.0643707 0 0.0624389 0.104336 0.177397 0 0 0 0 |***| 0 0 0 0 0 0 0 0 0.0761444 0.0861654 |***| 0.345712 0.0226614 0.360014 0.0812755 -0.948649 0.157355 0.0720559 0.196517 0.166788 0 |***| 0.0565064 0.0380366 0.029379 0 0.00504965 0.0883718 0 0 0.163034 0.116222 |***| 0.222225 0.166489 0.0955868 -0.0106412 0 0.000994925 
Weights: 
1e-09 0 0 0.924801 0.419386 0.23615 0 0 0 0 |***| 0 0 0 0 0 0 0 0 0 0 |***| 0 0 0 0 0 0.00980819 0.1406 0 0.132993 0 |***| 0 0.0547475 0 0 0 0.718451 0 0 0.140933 0.138128 |***| 0.22682 0 0.120578 -1.61843 0 -1.18799 
[0:58] Calculating q-values
[0:58] Number of IDs at 50%, 5%, 1%, 0.1% FDR: 3104, 1434, 766, 0
[0:58] Calculating q-values
[0:58] Number of IDs at 50%, 5%, 1%, 0.1% FDR: 3469, 1590, 1265, 0
[0:58] Switching back
[0:58] Reverting weights
[0:58] Precursor search
[0:58] Optimising weights
Averages: 
0.10571 0.0643707 0 0.0624389 0.104336 0.177397 0 0 0 0 |***| 0 0 0 0 0 0 0 0 0.0761444 0.0861654 |***| 0.345712 0.0226614 0.360014 0.0812755 -0.948649 0.157355 0.0720559 0.196517 0.166788 0 |***| 0.0565064 0.0380366 0.029379 0 0.00504965 0.0883718 0 0 0.163034 0.116222 |***| 0.222225 0.166489 0.0955868 -0.0106412 0 0.000994925 
Weights: 
0.301694 0 0 2.37399 0.700499 0.481534 0 0 0 0 |***| 0 0 0 0 0 0 0 0 0.529178 0 |***| 0 0 0 0 0 0 0.156605 0.0482205 0 0 |***| 0 0 0 0 0 0.409738 0 0 0.249663 0.113419 |***| 0.242923 0 0.0892463 -2.1569 0 -2.46221 
[0:58] Calculating q-values
[0:58] Number of IDs at 50%, 5%, 1%, 0.1% FDR: 3104, 1434, 766, 0
[0:58] Calculating q-values
[0:58] Number of IDs at 50%, 5%, 1%, 0.1% FDR: 3481, 1765, 1301, 1015
[0:58] Calibrating retention times
[0:58] 1301 precursors used for iRT estimation.
[0:58] Restoring classifier and weights to
0.369489 0 0 1.993 0.546429 0.368967 0 0 0 0 |***| 0 0 0 0 0 0 0 0 0.577785 0 |***| 0 0 0 0 0 0 0.106117 0.0445326 0 0 |***| 0 0 0 0 0 0.523857 0 0 0.214775 0.196422 |***| 0.341867 0 0.0267393 -2.80459 0 -1.72348 
[0:58] Precursor search
[1:04] Optimising weights
Averages: 
0.105702 0.0648535 0 0.0619712 0.103446 0.168947 0 0 0 0 |***| 0 0 0 0 0 0 0 0 0.0775446 0.0865139 |***| 0.349893 0.0226114 0.377909 0.0822097 -0.970129 0.147466 0.0751312 0.209503 0.161245 0 |***| 0.0568061 0.0366822 0.029767 0.0937622 0.00640783 0.0876835 0.0289149 0.208343 0.151467 0.103959 |***| 0.212365 0.162158 0.0913567 -0.0108363 0 0.00232676 
Weights: 
0.807821 0 0 1.97982 0.693677 0.396712 0 0 0 0 |***| 0 0 0 0 0 0 0 0 0.780959 0 |***| 0 0 0 0 0 0 0.380061 0.0154687 0 0 |***| 0.138613 0 0 0.388864 0 0 0 0 0.0157071 0 |***| 0.211975 0.0561012 0.0388283 -2.19704 0 -1.71334 
[1:04] Calculating q-values
[1:04] Number of IDs at 50%, 5%, 1%, 0.1% FDR: 48100, 20742, 13719, 7674
[1:04] Calculating q-values
[1:04] Number of IDs at 50%, 5%, 1%, 0.1% FDR: 51376, 26376, 19097, 10534
[1:04] Removing low confidence identifications
[1:04] Removing interfering precursors
[1:06] Calculating q-values
[1:06] Number of IDs at 50%, 5%, 1%, 0.1% FDR: 36892, 24342, 18386, 10370
[1:06] Calibrating retention times
[1:06] 18386 precursors used for iRT estimation.
[1:06] Optimising weights
Averages: 
0.155525 0.0937318 0 0.0900874 0.156246 0.262624 0 0 0 0 |***| 0 0 0 0 0 0 0 0 0.111754 0.12526 |***| 0.498931 0.0300992 0.51447 0.121746 -1.44248 0.189451 0.11207 0.30911 0.248506 0 |***| 0.0818143 0.0527047 0.0427575 0.144074 0.00636078 0.127115 0.0429142 0.305589 0.231729 0.158367 |***| 0.321049 0.245233 0.138329 -0.0156304 0 0.00216016 
Weights: 
0.728085 0 0 2.79139 1.1823 0.996758 0 0 0 0 |***| 0 0 0 0 0 0 0 0 0.652763 0 |***| 0 0 0 0 0 0.00384669 0.729148 0.00599242 0 0 |***| 0 0 0 0.611118 0 0.534183 0 0 0.0387745 0.00620107 |***| 0.332494 0.0967434 0.0816467 -3.73683 0 -2.95301 
[1:07] Training neural networks: 24492 targets, 35512 decoys
[1:10] Calculating q-values
[1:10] Number of IDs at 50%, 5%, 1%, 0.1% FDR: 41874, 27511, 22048, 14036
[1:10] Calibrating retention times
[1:10] 22048 precursors used for iRT estimation.
[1:10] Translating peaks within elution groups
[1:11] Calculating q-values
[1:11] Number of IDs at 50%, 5%, 1%, 0.1% FDR: 62290, 50064, 43546, 32103
[1:11] Scoring channels
[1:12] Precursors at 1% channel FDR: 27301 out of 37818
[1:12] Calculating protein q-values
[1:12] Number of genes identified at 1% FDR: 1897 (precursor-level), 1486 (protein-level) (inference performed using proteotypic peptides only)
[1:12] Quantification
[1:14] Quantification information saved to H:\JD\eJD1445.raw.quant.

[1:14] File #3/3
[1:14] Loading run H:\JD\eJD1444.raw
[1:35] Detected MS/MS range: 63.6687 - 2874.27
[1:36] Run loaded
[1:36] 146291 library precursors are potentially detectable
[1:36] Processing batch #1 out of 73 
[1:36] Precursor search
[1:36] Optimising weights
Averages: 
0.0428945 0.028575 0 0.0363553 0.0679923 0.0688497 
Weights: 
2.51912 0 0 1.62227 0.612166 0.292622 
[1:36] Calculating q-values
[1:36] Number of IDs at 50%, 5%, 1%, 0.1% FDR: 502, 208, 113, 0
[1:36] Calibrating retention times
[1:36] 113 precursors used for iRT estimation.
[1:36] Precursor search
[1:36] Optimising weights
Averages: 
0.0432134 0.0289203 0 0.034746 0.0624727 0.0886624 
Weights: 
1.1198 0.485137 0 2.48821 0.603701 0.660389 
[1:36] Calculating q-values
[1:36] Number of IDs at 50%, 5%, 1%, 0.1% FDR: 565, 208, 0, 0
[1:36] Calibrating retention times
[1:36] 50 precursors used for iRT estimation.
[1:36] Mass correction transform (207 precursors): -2.22946e-09 -0.00195121 4.79678e-06 
[1:36] M/z SD: 3.59345 ppm
[1:36] Top 70% mass accuracy: 3.54277 ppm
[1:36] Top 70% mass accuracy without correction: 3.81181ppm
[1:36] MS1 mass correction transform (173 precursors): -1.47861e-09 -0.00238516 5.72848e-06 
[1:36] Top 70% MS1 mass accuracy: 2.37164 ppm
[1:36] Top 70% MS1 mass accuracy without correction: 2.52884ppm
[1:36] Recalibrating with mass accuracy 1.77138e-05, 1.18582e-05 (MS2, MS1)
[1:36] Processing batch #1 out of 73 
[1:36] Precursor search
[1:37] Optimising weights
Averages: 
0.0474181 0.0316925 0 0.0401901 0.0755939 0.0947306 
Weights: 
1.00057 0 0 2.94476 0.75494 0.621716 
[1:37] Calculating q-values
[1:37] Number of IDs at 50%, 5%, 1%, 0.1% FDR: 543, 242, 0, 0
[1:37] Calibrating retention times
[1:37] 50 precursors used for iRT estimation.
[1:37] Processing batch #2 out of 73 
[1:37] Precursor search
[1:38] Optimising weights
Averages: 
0.0456688 0.0297523 0 0.0378345 0.0732175 0.0923608 
Weights: 
1.28005 0 0 2.57772 0.767615 0.582301 
[1:38] Calculating q-values
[1:38] Number of IDs at 50%, 5%, 1%, 0.1% FDR: 1038, 452, 319, 0
[1:38] Calibrating retention times
[1:38] 319 precursors used for iRT estimation.
[1:38] Processing batch #3 out of 73 
[1:38] Precursor search
[1:39] Optimising weights
Averages: 
0.0451091 0.0288198 0 0.0348166 0.071708 0.0885834 
Weights: 
1.23922 0 0 2.4844 0.94123 0.627919 
[1:39] Calculating q-values
[1:39] Number of IDs at 50%, 5%, 1%, 0.1% FDR: 1578, 643, 436, 0
[1:39] Calibrating retention times
[1:39] 436 precursors used for iRT estimation.
[1:39] Processing batch #4 out of 73 
[1:39] Precursor search
[1:39] Optimising weights
Averages: 
0.0452021 0.0292489 0 0.0357219 0.0692941 0.0884437 
Weights: 
0.926913 0 0 2.78973 0.907838 0.670917 
[1:39] Calculating q-values
[1:39] Number of IDs at 50%, 5%, 1%, 0.1% FDR: 2116, 868, 621, 0
[1:39] Calibrating retention times
[1:39] 621 precursors used for iRT estimation.
[1:39] Processing batch #5 out of 73 
[1:39] Precursor search
[1:40] Optimising weights
Averages: 
0.0437787 0.0269466 0 0.0332861 0.0670157 0.0908076 
Weights: 
0.897528 0 0 2.65245 0.893684 0.692774 
[1:40] Calculating q-values
[1:40] Number of IDs at 50%, 5%, 1%, 0.1% FDR: 2619, 1033, 721, 0
[1:40] Calibrating retention times
[1:40] 721 precursors used for iRT estimation.
[1:40] Precursor search
[1:40] Optimising weights
Averages: 
0.0437412 0.027096 0 0.0335506 0.0669502 0.0899056 
Weights: 
0.844494 0 0 2.67725 0.889309 0.714296 
[1:40] Calculating q-values
[1:40] Number of IDs at 50%, 5%, 1%, 0.1% FDR: 2635, 1029, 730, 0
[1:40] Precursor search
[1:40] Optimising weights
Averages: 
0.0437714 0.0273102 0 0.0336178 0.0670764 0.0894632 
Weights: 
0.840701 0 0 2.67211 0.889986 0.717816 
[1:40] Calculating q-values
[1:40] Number of IDs at 50%, 5%, 1%, 0.1% FDR: 2635, 1029, 732, 0
[1:40] Precursor search
[1:40] Optimising weights
Averages: 
0.0437641 0.0273202 0 0.033531 0.0672557 0.0895738 
Weights: 
0.851146 0 0 2.64324 0.897512 0.717907 
[1:40] Calculating q-values
[1:40] Number of IDs at 50%, 5%, 1%, 0.1% FDR: 2634, 1031, 732, 0
[1:40] Calibrating retention times
[1:40] 732 precursors used for iRT estimation.
[1:40] RT window set to 1.63289
[1:40] Mass correction transform (1022 precursors): -1.94016e-09 -0.00170178 4.34063e-06 
[1:40] M/z SD: 3.21531 ppm
[1:40] Top 70% mass accuracy: 3.84403 ppm
[1:40] Top 70% mass accuracy without correction: 3.87738ppm
[1:40] MS1 mass correction transform (822 precursors): -3.72915e-09 -0.00282538 7.98167e-06 
[1:40] Top 70% MS1 mass accuracy: 2.20314 ppm
[1:40] Top 70% MS1 mass accuracy without correction: 2.48523ppm
[1:40] Refining mass correction
[1:40] Calibrating retention times
[1:40] Mass correction transform (716 precursors): -1.22987e-09 -0.00149682 3.16336e-06 
[1:40] M/z SD: 3.26548 ppm
[1:40] Top 70% mass accuracy: 3.79905 ppm
[1:40] Top 70% mass accuracy without correction: 3.87738ppm
[1:40] MS1 mass correction transform (575 precursors): -2.83606e-09 -0.00246682 6.83851e-06 
[1:40] Top 70% MS1 mass accuracy: 2.20739 ppm
[1:40] Top 70% MS1 mass accuracy without correction: 2.48523ppm
[1:40] Recommended MS1 mass accuracy setting: 11.037 ppm
[1:40] Using mass accuracy 1e-05, 5e-06 (MS2, MS1)
[1:40] Processing batch #1 out of 73 
[1:40] Precursor search
[1:41] Optimising weights
Averages: 
0.1118 0.0684861 0 0.0619571 0.110523 0.171642 
Weights: 
2.09395 0.335876 0 1.33029 0.826167 0.553794 
[1:41] Calculating q-values
[1:41] Number of IDs at 50%, 5%, 1%, 0.1% FDR: 701, 341, 245, 0
[1:41] Calibrating retention times
[1:41] 245 precursors used for iRT estimation.
[1:41] Processing batch #2 out of 73 
[1:41] Precursor search
[1:41] Optimising weights
Averages: 
0.106187 0.0662668 0 0.0628066 0.10977 0.167717 
Weights: 
1.80117 0 0 2.0236 0.92137 0.602099 
[1:41] Calculating q-values
[1:41] Number of IDs at 50%, 5%, 1%, 0.1% FDR: 1312, 666, 467, 0
[1:41] Calibrating retention times
[1:41] 467 precursors used for iRT estimation.
[1:41] Processing batch #3 out of 73 
[1:41] Precursor search
[1:41] Optimising weights
Averages: 
0.107014 0.0664039 0 0.0623839 0.109809 0.166798 
Weights: 
1.89067 0.122221 0 1.9297 0.957331 0.580376 
[1:41] Calculating q-values
[1:41] Number of IDs at 50%, 5%, 1%, 0.1% FDR: 2023, 989, 670, 0
[1:41] Calibrating retention times
[1:41] 670 precursors used for iRT estimation.
[1:41] Processing batch #4 out of 73 
[1:41] Precursor search
[1:41] Optimising weights
Averages: 
0.106875 0.0665004 0 0.0623703 0.1075 0.167917 
Weights: 
1.81271 0.1776 0 1.98503 0.921675 0.592335 
[1:41] Calculating q-values
[1:41] Number of IDs at 50%, 5%, 1%, 0.1% FDR: 2677, 1353, 904, 0
[1:41] Calibrating retention times
[1:41] 904 precursors used for iRT estimation.
[1:41] Processing batch #5 out of 73 
[1:41] Precursor search
[1:41] Optimising weights
Averages: 
0.104086 0.0647044 0 0.0615517 0.102894 0.162978 
Weights: 
1.81797 0.0582715 0 2.28091 0.840527 0.591126 
[1:41] Calculating q-values
[1:41] Number of IDs at 50%, 5%, 1%, 0.1% FDR: 3383, 1654, 1095, 0
[1:41] Calibrating retention times
[1:41] 1095 precursors used for iRT estimation.
[1:41] Precursor search
[1:41] Optimising weights
Averages: 
0.104633 0.064724 0 0.0606546 0.104125 0.166059 0 0 0 0 |***| 0 0 0 0 0 0 0 0 0.0769563 0.0859288 |***| 0.348889 0.0222573 0.373088 0.0798573 -0.969753 0.134995 0.0759864 0.212295 0.158931 0 |***| 0.0567269 0.0366665 0.0294086 0 0.00749998 0.0873866 0 0 0.164417 0.116818 |***| 0.229031 0.177951 0.0996541 -0.0183664 0 -0.000964686 
Weights: 
0.0814354 0 0 0.510723 0.439168 0.0169098 0 0 0 0 |***| 0 0 0 0 0 0 0 0 0.199611 0.766905 |***| 0 0 0 0 0 0.0058987 0.181068 0.0140523 0.223506 0 |***| 0 0 0 0 0 0 0 0 0.268193 0.0681325 |***| 0.154993 0.0662034 0.0487071 -1.30069 0 -0.600441 
[1:41] Calculating q-values
[1:41] Number of IDs at 50%, 5%, 1%, 0.1% FDR: 3414, 1750, 1259, 0
[1:41] Calibrating retention times
[1:41] 1259 precursors used for iRT estimation.
[1:41] Precursor search
[1:41] Optimising weights
Averages: 
0.108842 0.0680795 0 0.0656755 0.106633 0.165509 0 0 0 0 |***| 0 0 0 0 0 0 0 0 0.0812575 0.0888844 |***| 0.367017 0.0222022 0.415799 0.0834435 -1.01187 0.0857935 0.0726518 0.209004 0.151286 0 |***| 0.0597485 0.0377312 0.0319644 0 0.00802418 0.0913307 0 0 0.150123 0.108428 |***| 0.210761 0.164911 0.0907574 -0.0121366 0 0.0016613 
Weights: 
0.0149901 0 0 0.759371 0.401548 0.0354758 0 0 0 0 |***| 0 0 0 0 0 0 0 0 0.189796 0.545911 |***| 0 0 0 0 0 0.0116515 0.146068 0.030869 0.298307 0 |***| 0 0 0 0 0 0.290616 0 0 0.219962 0.109824 |***| 0.11393 0.0680432 0.028173 -1.4554 0 -0.832028 
[1:41] Calculating q-values
[1:41] Number of IDs at 50%, 5%, 1%, 0.1% FDR: 3403, 1757, 1316, 0
[1:41] Calibrating retention times
[1:41] 1316 precursors used for iRT estimation.
[1:41] Precursor search
[1:41] Optimising weights
Averages: 
0.10943 0.0683874 0 0.0648273 0.109202 0.165615 0 0 0 0 |***| 0 0 0 0 0 0 0 0 0.0818277 0.0889914 |***| 0.368524 0.0227432 0.410946 0.0859622 -1.02943 0.0632884 0.0748906 0.214397 0.148981 0 |***| 0.0601382 0.0380709 0.0320364 0 0.00928998 0.0909141 0 0 0.150357 0.108346 |***| 0.213635 0.167202 0.0932501 -0.0124319 0 0.00258673 
Weights: 
0.0939445 0 0 0.486102 0.436589 0.0635096 0 0 0 0 |***| 0 0 0 0 0 0 0 0 0.183104 0.634719 |***| 0 0 0 0 0 0.00909976 0.166817 0.0340435 0.274224 0 |***| 0.0391285 0 0 0 0 0.227999 0 0 0.187239 0.113454 |***| 0.113369 0.0728845 0.0461365 -1.59118 0 -0.643681 
[1:41] Calculating q-values
[1:41] Number of IDs at 50%, 5%, 1%, 0.1% FDR: 3423, 1750, 1316, 1006
[1:41] Trying the other linear classifier
[1:41] Precursor search
[1:41] Optimising weights
Averages: 
0.108901 0.06818 0 0.0657168 0.106907 0.166427 0 0 0 0 |***| 0 0 0 0 0 0 0 0 0.0811629 0.0889243 |***| 0.366405 0.0220812 0.413948 0.0837641 -1.01429 0.0784012 0.0726666 0.208378 0.151838 0 |***| 0.0597936 0.0380419 0.0319615 0 0.00845749 0.0912775 0 0 0.149714 0.107469 |***| 0.211845 0.165378 0.0912004 -0.012543 0 0.00162952 
Weights: 
0.605661 0.251724 0 2.34462 0.709519 0.416905 0 0 0 0 |***| 0 0 0 0 0 0 0 0 0.814458 0.00564928 |***| 0 0 0 0 0 0 0.00916798 0.0814888 0 0 |***| 0.0327098 0 0 0 0 0 0 0 0.240814 0.0469026 |***| 0.130205 0.083262 0 -2.52408 0 -1.96832 
[1:41] Calculating q-values
[1:41] Number of IDs at 50%, 5%, 1%, 0.1% FDR: 3497, 1781, 1391, 0
[1:41] Calibrating retention times
[1:41] 1391 precursors used for iRT estimation.
[1:41] Precursor search
[1:41] Optimising weights
Averages: 
0.107527 0.0652816 0 0.0615487 0.107967 0.17735 0 0 0 0 |***| 0 0 0 0 0 0 0 0 0.0779683 0.0877221 |***| 0.345197 0.0221157 0.385593 0.0842748 -1.01325 0.165647 0.0739485 0.206371 0.170282 0 |***| 0.0575102 0.0368268 0.0304178 0 0.00756748 0.0892869 0 0 0.161647 0.115296 |***| 0.223194 0.174228 0.0968444 -0.0112146 0 0.0029317 
Weights: 
0.73035 0 0 1.8989 0.829524 0.405319 0 0 0 0 |***| 0 0 0 0 0 0 0 0 0.739069 0.0477611 |***| 0 0 0 0 0 0 0 0.0866001 0 0 |***| 0.0687711 0 0 0 0 0 0 0 0.258787 0.0642064 |***| 0.183219 0.126631 0 -2.22966 0 -1.39216 
[1:41] Calculating q-values
[1:41] Number of IDs at 50%, 5%, 1%, 0.1% FDR: 3251, 1503, 1027, 0
[1:41] Calculating q-values
[1:41] Number of IDs at 50%, 5%, 1%, 0.1% FDR: 3467, 1767, 1321, 0
[1:41] Trying the other linear classifier
[1:41] Precursor search
[1:41] Optimising weights
Averages: 
0.108959 0.0681993 0 0.066059 0.106681 0.166321 0 0 0 0 |***| 0 0 0 0 0 0 0 0 0.0812388 0.0890517 |***| 0.366092 0.0222683 0.411306 0.0835269 -1.01115 0.0782816 0.0725005 0.207766 0.151914 0 |***| 0.0597933 0.0376158 0.0319798 0 0.00810377 0.0913419 0 0 0.148721 0.107348 |***| 0.211693 0.165011 0.0910885 -0.0126621 0 0.00152643 
Weights: 
0.0138912 0 0 0.824619 0.399333 0.0738682 0 0 0 0 |***| 0 0 0 0 0 0 0 0 0.142228 0.676021 |***| 0 0 0 0 0 0.00777397 0.154284 0.0214628 0.28338 0 |***| 0 0 0 0 0 0.175681 0 0 0.192874 0.104309 |***| 0.11821 0.0624326 0.0341576 -1.5578 0 -0.862714 
[1:41] Calculating q-values
[1:41] Number of IDs at 50%, 5%, 1%, 0.1% FDR: 3282, 1491, 1027, 0
[1:41] Calculating q-values
[1:41] Number of IDs at 50%, 5%, 1%, 0.1% FDR: 3398, 1748, 1328, 0
[1:41] Switching back
[1:41] Reverting weights
[1:41] Precursor search
[1:42] Optimising weights
Averages: 
0.108959 0.0681993 0 0.066059 0.106681 0.166321 0 0 0 0 |***| 0 0 0 0 0 0 0 0 0.0812388 0.0890517 |***| 0.366092 0.0222683 0.411306 0.0835269 -1.01115 0.0782816 0.0725005 0.207766 0.151914 0 |***| 0.0597933 0.0376158 0.0319798 0 0.00810377 0.0913419 0 0 0.148721 0.107348 |***| 0.211693 0.165011 0.0910885 -0.0126621 0 0.00152643 
Weights: 
0.61053 0 0 2.53888 0.693765 0.426969 0 0 0 0 |***| 0 0 0 0 0 0 0 0 0.831205 0.0918898 |***| 0 0 0 0 0 0 0.0263282 0.0678715 0 0 |***| 0.0136581 0 0 0 0 0 0 0 0.218936 0.0557773 |***| 0.133905 0.0821196 0 -2.52929 0 -2.0086 
[1:42] Calculating q-values
[1:42] Number of IDs at 50%, 5%, 1%, 0.1% FDR: 3282, 1491, 1027, 0
[1:42] Calculating q-values
[1:42] Number of IDs at 50%, 5%, 1%, 0.1% FDR: 3509, 1784, 1388, 1004
[1:42] Calibrating retention times
[1:42] 1388 precursors used for iRT estimation.
[1:42] Restoring classifier and weights to
0.0814354 0 0 0.510723 0.439168 0.0169098 0 0 0 0 |***| 0 0 0 0 0 0 0 0 0.199611 0.766905 |***| 0 0 0 0 0 0.0058987 0.181068 0.0140523 0.223506 0 |***| 0 0 0 0 0 0 0 0 0.268193 0.0681325 |***| 0.154993 0.0662034 0.0487071 -1.30069 0 -0.600441 
[1:42] Precursor search
[1:47] Optimising weights
Averages: 
0.107647 0.0676129 0 0.0653189 0.103976 0.164984 0 0 0 0 |***| 0 0 0 0 0 0 0 0 0.0801499 0.0872459 |***| 0.369521 0.0223745 0.394262 0.0824883 -0.976911 0.0512363 0.0741949 0.212729 0.151332 0 |***| 0.0588446 0.0376425 0.030859 0.0919087 0.00862218 0.0903185 0.0290457 0.214342 0.143871 0.0999658 |***| 0.215071 0.166106 0.0926902 -0.0129976 0 0.0020667 
Weights: 
0.723524 0 0 2.29475 0.675973 0.443444 0 0 0 0 |***| 0 0 0 0 0 0 0 0 0.722262 0 |***| 0 0 0 0 0 0 0.292121 0.0335091 0 0 |***| 0.161678 0 0 0.305528 0 0.0748404 0 0 0 0 |***| 0.184219 0.105803 0 -2.63487 0 -1.80378 
[1:47] Calculating q-values
[1:47] Number of IDs at 50%, 5%, 1%, 0.1% FDR: 48895, 21569, 14463, 8081
[1:47] Calculating q-values
[1:47] Number of IDs at 50%, 5%, 1%, 0.1% FDR: 52706, 26687, 19414, 12265
[1:47] Removing low confidence identifications
[1:47] Removing interfering precursors
[1:49] Calculating q-values
[1:49] Number of IDs at 50%, 5%, 1%, 0.1% FDR: 38033, 24645, 18634, 12062
[1:49] Calibrating retention times
[1:49] 18634 precursors used for iRT estimation.
[1:50] Optimising weights
Averages: 
0.154336 0.0959342 0 0.0931332 0.150779 0.24798 0 0 0 0 |***| 0 0 0 0 0 0 0 0 0.113006 0.124037 |***| 0.518519 0.0300531 0.530545 0.117428 -1.39211 0.0501564 0.107264 0.302302 0.225841 0 |***| 0.0829913 0.0532894 0.0438397 0.136742 0.0093452 0.128599 0.0421414 0.308347 0.213358 0.147172 |***| 0.315115 0.243148 0.136002 -0.0185606 0 0.0014446 
Weights: 
0.595927 0 0 3.31878 1.05483 1.04399 0 0 0 0 |***| 0 0 0 0 0 0 0 0 0.523677 0 |***| 0 0 0 0 0 0 0.621913 0 0 0 |***| 0 0 0 0.486485 0 0.728816 0 0 0 0 |***| 0.30171 0.16156 0.000439057 -4.36535 0 -3.13883 
[1:50] Training neural networks: 24884 targets, 35550 decoys
[1:53] Calculating q-values
[1:53] Number of IDs at 50%, 5%, 1%, 0.1% FDR: 42818, 27963, 22341, 15130
[1:53] Calibrating retention times
[1:53] 22341 precursors used for iRT estimation.
[1:53] Translating peaks within elution groups
[1:54] Calculating q-values
[1:54] Number of IDs at 50%, 5%, 1%, 0.1% FDR: 63215, 50762, 44084, 33722
[1:54] Scoring channels
[1:55] Precursors at 1% channel FDR: 27780 out of 38503
[1:55] Calculating protein q-values
[1:55] Number of genes identified at 1% FDR: 1924 (precursor-level), 1529 (protein-level) (inference performed using proteotypic peptides only)
[1:55] Quantification
[1:57] Quantification information saved to H:\JD\eJD1444.raw.quant.

[1:57] Cross-run analysis
[1:57] Reading quantification information: 3 files
[1:57] Quantifying peptides
[1:58] Assembling protein groups
[1:58] Quantifying proteins
[1:58] Calculating q-values for protein and gene groups
[1:58] Calculating global q-values for protein and gene groups
[1:58] Writing report
[2:09] Report saved to H:\JD\eJD1443_45\Report-first-pass.tsv.
[2:09] Saving precursor levels matrix
[2:09] Precursor levels matrix (1% precursor and protein group FDR) saved to H:\JD\eJD1443_45\Report-first-pass.pr_matrix.tsv.
[2:09] Saving precursor levels matrix
[2:09] Precursor levels matrix (1% precursor and protein group FDR) saved to H:\JD\eJD1443_45\Report-first-pass.pr_matrix_channels.tsv.
[2:09] Saving precursor levels matrix
[2:10] Precursor levels matrix (1% precursor and protein group FDR) saved to H:\JD\eJD1443_45\Report-first-pass.pr_matrix_channels_translated.tsv.
[2:10] Saving precursor levels matrix
[2:10] Precursor levels matrix (1% precursor and protein group FDR) saved to H:\JD\eJD1443_45\Report-first-pass.pr_matrix_channels_ms1.tsv.
[2:10] Saving precursor levels matrix
[2:10] Precursor levels matrix (1% precursor and protein group FDR) saved to H:\JD\eJD1443_45\Report-first-pass.pr_matrix_channels_ms1_translated.tsv.
[2:10] Saving precursor levels matrix
[2:10] Precursor levels matrix (1% precursor and protein group FDR) saved to H:\JD\eJD1443_45\Report-first-pass.pr_matrix_channels_ms1_extracted.tsv.
[2:10] Saving protein group levels matrix
[2:10] Protein group levels matrix (1% precursor FDR and protein group FDR) saved to H:\JD\eJD1443_45\Report-first-pass.pg_matrix.tsv.
[2:10] Saving gene group levels matrix
[2:10] Gene groups levels matrix (1% precursor FDR and protein group FDR) saved to H:\JD\eJD1443_45\Report-first-pass.gg_matrix.tsv.
[2:10] Saving unique genes levels matrix
[2:10] Unique genes levels matrix (1% precursor FDR and protein group FDR) saved to H:\JD\eJD1443_45\Report-first-pass.unique_genes_matrix.tsv.
[2:10] Stats report saved to H:\JD\eJD1443_45\Report-first-pass.stats.tsv
[2:10] Generating spectral library:
[2:10] 19118 precursors passing the FDR threshold are to be extracted
[2:10] Loading run H:\JD\eJD1443.raw
[2:14] Detected MS/MS range: 63.6681 - 2868.58
[2:14] Run loaded
[2:14] 146291 library precursors are potentially detectable
[2:14] 2445 spectra added to the library
[2:14] Loading run H:\JD\eJD1445.raw
[2:18] Detected MS/MS range: 63.6705 - 2872.88
[2:18] Run loaded
[2:18] 146291 library precursors are potentially detectable
[2:18] 2558 spectra added to the library
[2:18] Loading run H:\JD\eJD1444.raw
[2:22] Detected MS/MS range: 63.6687 - 2874.27
[2:22] Run loaded
[2:22] 146291 library precursors are potentially detectable
[2:23] 4819 spectra added to the library
[2:23] Assembling elution groups
[2:23] Saving spectral library to H:\JD\eJD1443_45\Report-lib.tsv
[2:25] 19118 precursors saved
[2:25] Loading the generated library and saving it in the .speclib format
[2:25] Loading spectral library H:\JD\eJD1443_45\Report-lib.tsv
[2:26] Spectral library loaded: 4907 protein isoforms, 2986 protein groups and 19118 precursors in 9173 elution groups.
[2:26] Loading protein annotations from FASTA F:\JD\plexDIA\swissprot_human_CanIso_02142022.fasta
[2:28] Gene names missing for some isoforms
[2:28] Library contains 2504 proteins, and 2500 genes
[2:28] Saving the library to H:\JD\eJD1443_45\Report-lib.tsv.speclib
[2:28] Splitting library entries across channels
[2:28] Assembling elution groups

[2:28] Second pass: using the newly created spectral library to reanalyse the data
[2:28] File #1/3
[2:28] Loading run H:\JD\eJD1443.raw
[2:32] Detected MS/MS range: 63.6681 - 2868.58
[2:32] Run loaded
[2:32] 76350 library precursors are potentially detectable
[2:32] Processing batch #1 out of 38 
[2:32] Precursor search
[2:33] Optimising weights
Averages: 
0.0198332 0.0109632 0 0.0167109 0.0684095 0.0456806 
Weights: 
1.10832 0 0 1.27783 1.19991 0.332898 
[2:33] Calculating q-values
[2:33] Number of IDs at 50%, 5%, 1%, 0.1% FDR: 388, 63, 0, 0
[2:33] Calibrating retention times
[2:33] 50 precursors used for iRT estimation.
[2:33] Processing batch #2 out of 38 
[2:33] Precursor search
[2:34] Optimising weights
Averages: 
0.0197317 0.0110687 0 0.0165739 0.0560298 0.0523937 
Weights: 
0.928828 0 0 1.50095 0.884432 0.420745 
[2:34] Calculating q-values
[2:34] Number of IDs at 50%, 5%, 1%, 0.1% FDR: 707, 153, 0, 0
[2:34] Calibrating retention times
[2:34] 50 precursors used for iRT estimation.
[2:34] Precursor search
[2:34] Optimising weights
Averages: 
0.0197317 0.0110687 0 0.0165739 0.0560298 0.0523937 
Weights: 
0.928828 0 0 1.50095 0.884432 0.420745 
[2:34] Calculating q-values
[2:34] Number of IDs at 50%, 5%, 1%, 0.1% FDR: 707, 153, 0, 0
[2:34] Calibrating retention times
[2:34] 50 precursors used for iRT estimation.
[2:34] Mass correction transform (153 precursors): 8.33019e-09 0.00126273 -9.03946e-06 
[2:34] M/z SD: 7.21926 ppm
[2:34] Top 70% mass accuracy: 2.4833 ppm
[2:34] Top 70% mass accuracy without correction: 2.849ppm
[2:34] MS1 mass correction transform (131 precursors): -3.1502e-09 -0.00276308 7.06406e-06 
[2:34] Top 70% MS1 mass accuracy: 2.30587 ppm
[2:34] Top 70% MS1 mass accuracy without correction: 2.44475ppm
[2:34] Recalibrating with mass accuracy 1.24165e-05, 1.15294e-05 (MS2, MS1)
[2:34] Processing batch #1 out of 38 
[2:34] Precursor search
[2:35] Optimising weights
Averages: 
0.0872714 0.0628275 0 0.0770132 0.153081 0.137493 
Weights: 
0.259873 0.622095 0 4.4341 1.44 0.807997 
[2:35] Calculating q-values
[2:35] Number of IDs at 50%, 5%, 1%, 0.1% FDR: 852, 443, 238, 0
[2:35] Calibrating retention times
[2:35] 238 precursors used for iRT estimation.
[2:35] Processing batch #2 out of 38 
[2:35] Precursor search
[2:35] Optimising weights
Averages: 
0.0945786 0.055644 0 0.0700378 0.134667 0.169366 
Weights: 
2.42975 0 0 3.23479 1.21707 0.656394 
[2:35] Calculating q-values
[2:35] Number of IDs at 50%, 5%, 1%, 0.1% FDR: 1772, 925, 607, 0
[2:35] Calibrating retention times
[2:35] 607 precursors used for iRT estimation.
[2:35] Processing batch #3 out of 38 
[2:35] Precursor search
[2:36] Optimising weights
Averages: 
0.0935293 0.0573545 0 0.0720489 0.138217 0.164806 
Weights: 
2.34814 0 0 3.31736 1.2298 0.720946 
[2:36] Calculating q-values
[2:36] Number of IDs at 50%, 5%, 1%, 0.1% FDR: 2681, 1387, 960, 0
[2:36] Calibrating retention times
[2:36] 960 precursors used for iRT estimation.
[2:36] Precursor search
[2:36] Optimising weights
Averages: 
0.0935592 0.057634 0 0.0723195 0.138643 0.162668 
Weights: 
2.38591 0 0 3.27381 1.23299 0.716338 
[2:36] Calculating q-values
[2:36] Number of IDs at 50%, 5%, 1%, 0.1% FDR: 2678, 1381, 958, 0
[2:36] Precursor search
[2:36] Optimising weights
Averages: 
0.0935419 0.057685 0 0.0724328 0.138622 0.162239 
Weights: 
2.37951 0 0 3.28919 1.2312 0.714705 
[2:36] Calculating q-values
[2:36] Number of IDs at 50%, 5%, 1%, 0.1% FDR: 2680, 1383, 957, 0
[2:36] Precursor search
[2:36] Optimising weights
Averages: 
0.0935487 0.0576246 0 0.0723564 0.138669 0.162487 
Weights: 
2.39123 0 0 3.27041 1.23447 0.713529 
[2:36] Calculating q-values
[2:36] Number of IDs at 50%, 5%, 1%, 0.1% FDR: 2683, 1380, 958, 0
[2:36] Calibrating retention times
[2:36] 958 precursors used for iRT estimation.
[2:36] RT window set to 0.683426
[2:36] Mass correction transform (1360 precursors): -1.09384e-10 -0.00122173 1.67253e-06 
[2:36] M/z SD: 2.73354 ppm
[2:36] Top 70% mass accuracy: 3.62686 ppm
[2:36] Top 70% mass accuracy without correction: 3.80288ppm
[2:36] MS1 mass correction transform (1051 precursors): -2.47497e-09 -0.00289663 7.02297e-06 
[2:36] Top 70% MS1 mass accuracy: 2.02369 ppm
[2:36] Top 70% MS1 mass accuracy without correction: 2.17987ppm
[2:36] Refining mass correction
[2:36] Calibrating retention times
[2:36] Mass correction transform (951 precursors): -4.79887e-10 -0.00121643 1.70264e-06 
[2:36] M/z SD: 2.75609 ppm
[2:36] Top 70% mass accuracy: 3.69974 ppm
[2:36] Top 70% mass accuracy without correction: 3.80288ppm
[2:36] MS1 mass correction transform (735 precursors): -1.62936e-09 -0.00233953 5.50469e-06 
[2:36] Top 70% MS1 mass accuracy: 2.02527 ppm
[2:36] Top 70% MS1 mass accuracy without correction: 2.17987ppm
[2:36] Recommended MS1 mass accuracy setting: 10.1263 ppm
[2:36] Using mass accuracy 1e-05, 5e-06 (MS2, MS1)
[2:36] Processing batch #1 out of 38 
[2:36] Precursor search
[2:36] Optimising weights
Averages: 
0.191357 0.117747 0 0.122248 0.218864 0.303962 
Weights: 
1.68553 0 0 3.14578 1.98911 1.22661 
[2:36] Calculating q-values
[2:36] Number of IDs at 50%, 5%, 1%, 0.1% FDR: 1034, 669, 538, 0
[2:36] Calibrating retention times
[2:36] 538 precursors used for iRT estimation.
[2:36] Processing batch #2 out of 38 
[2:36] Precursor search
[2:36] Optimising weights
Averages: 
0.196587 0.121327 0 0.126434 0.212047 0.301647 
Weights: 
1.95974 0 0 3.56365 1.55603 1.14744 
[2:36] Calculating q-values
[2:36] Number of IDs at 50%, 5%, 1%, 0.1% FDR: 2084, 1361, 1117, 0
[2:36] Calibrating retention times
[2:36] 1117 precursors used for iRT estimation.
[2:36] Processing batch #3 out of 38 
[2:36] Precursor search
[2:36] Optimising weights
Averages: 
0.199801 0.12458 0 0.128268 0.215147 0.309962 
Weights: 
1.85116 0.100167 0 3.83096 1.51413 1.20036 
[2:36] Calculating q-values
[2:36] Number of IDs at 50%, 5%, 1%, 0.1% FDR: 3145, 2092, 1505, 1209
[2:36] Calibrating retention times
[2:36] 1505 precursors used for iRT estimation.
[2:36] Precursor search
[2:36] Optimising weights
Averages: 
0.199546 0.124864 0 0.12816 0.215347 0.308947 0 0 0 0 |***| 0 0 0 0 0 0 0 0 0.148055 0.161098 |***| 0.7323 0.0533586 0.827977 0.16418 -1.90057 0.173835 0.149157 0.410408 0.280792 0 |***| 0.116637 0.078834 0.0640305 0 0.0204231 0.166385 0 0 0.280903 0.195168 |***| 0.407092 0.311247 0.172891 -0.0438955 0 0.00229575 
Weights: 
0.0512043 0 0 1.11215 0.589881 0.500013 0 0 0 0 |***| 0 0 0 0 0 0 0 0 0 0 |***| 0 0 0 0 0 0.0441312 0.253502 0 0.0240761 0 |***| 0.015833 0 0 0 0.351188 1.4334 0 0 0.178485 0 |***| 0.0604436 0 0.0218223 -7.33573 0 -1.33642 
[2:36] Calculating q-values
[2:36] Number of IDs at 50%, 5%, 1%, 0.1% FDR: 3228, 2190, 1665, 1246
[2:36] Calibrating retention times
[2:36] 1665 precursors used for iRT estimation.
[2:36] Precursor search
[2:36] Optimising weights
Averages: 
0.206491 0.131051 0 0.135195 0.225508 0.306332 0 0 0 0 |***| 0 0 0 0 0 0 0 0 0.155435 0.165116 |***| 0.761208 0.0544854 0.860369 0.17563 -2.02069 0.0200159 0.158604 0.432939 0.274696 0 |***| 0.120348 0.0807984 0.0672902 0 0.0201894 0.170744 0 0 0.272065 0.1909 |***| 0.402147 0.304728 0.166994 -0.0386542 0 0.00503324 
Weights: 
0.129624 0 0 1.43786 0.488305 0.394276 0 0 0 0 |***| 0 0 0 0 0 0 0 0 0.122291 0 |***| 0 0 0 0 0 0.0270027 0.463086 0 0.0855082 0 |***| 0.108616 0 0 0 0.00763664 1.17073 0 0 0.0848615 0.0933169 |***| 0.0817261 0 0.045245 -7.58648 0 -1.71722 
[2:36] Calculating q-values
[2:36] Number of IDs at 50%, 5%, 1%, 0.1% FDR: 3278, 2244, 1665, 1321
[2:36] Calibrating retention times
[2:36] 1665 precursors used for iRT estimation.
[2:36] Precursor search
[2:36] Optimising weights
Averages: 
0.206461 0.129385 0 0.133634 0.225721 0.310889 0 0 0 0 |***| 0 0 0 0 0 0 0 0 0.154646 0.164893 |***| 0.75444 0.0551652 0.85504 0.175836 -1.99194 0.0834713 0.15184 0.418069 0.279477 0 |***| 0.119493 0.0807887 0.0667585 0 0.0227998 0.169899 0 0 0.27462 0.190402 |***| 0.403773 0.306894 0.169017 -0.0387237 0 0.00519591 
Weights: 
0.201424 0.0301304 0 1.26858 0.563426 0.364503 0 0 0 0 |***| 0 0 0 0 0 0 0 0 0.223234 0 |***| 0 0 0 0 0 0.0304223 0.365516 0 0.122331 0 |***| 0.055982 0 0 0 0.107299 1.06313 0 0 0.122369 0.0450826 |***| 0.0796634 0.010106 0.0521786 -7.66138 0 -1.72723 
[2:36] Calculating q-values
[2:36] Number of IDs at 50%, 5%, 1%, 0.1% FDR: 3269, 2239, 1660, 1371
[2:36] Trying the other linear classifier
[2:36] Precursor search
[2:36] Optimising weights
Averages: 
0.206059 0.130786 0 0.135281 0.224877 0.305492 0 0 0 0 |***| 0 0 0 0 0 0 0 0 0.155144 0.165027 |***| 0.759428 0.054407 0.861664 0.175157 -2.01678 0.0308034 0.158523 0.430505 0.274639 0 |***| 0.120177 0.0807388 0.0671618 0 0.0195391 0.170499 0 0 0.272287 0.19019 |***| 0.400995 0.304608 0.167489 -0.0387859 0 0.00504658 
Weights: 
0.859428 0 0 2.88175 1.23961 0.504234 0 0 0 0 |***| 0 0 0 0 0 0 0 0 0.889903 0 |***| 0 0 0 0 0 0.0111403 0.8225 0 0 0 |***| 0 0 0 0 0 0.778394 0 0 0 0 |***| 0 0 0 -14.7598 0 -3.50893 
[2:36] Calculating q-values
[2:36] Number of IDs at 50%, 5%, 1%, 0.1% FDR: 3296, 2328, 1875, 1347
[2:36] Calibrating retention times
[2:36] 1875 precursors used for iRT estimation.
[2:36] Precursor search
[2:36] Optimising weights
Averages: 
0.206189 0.128377 0 0.131953 0.222968 0.323449 0 0 0 0 |***| 0 0 0 0 0 0 0 0 0.152281 0.164353 |***| 0.744857 0.0544225 0.855 0.172199 -1.97344 0.137793 0.150527 0.413112 0.293071 0 |***| 0.118328 0.0799144 0.0662749 0 0.0212325 0.169953 0 0 0.286002 0.19685 |***| 0.41413 0.31236 0.173754 -0.0382322 0 0.00523491 
Weights: 
0.713218 0 0 2.68364 1.35897 0.635195 0 0 0 0 |***| 0 0 0 0 0 0 0 0 0.860979 0 |***| 0 0 0 0 0 0.0130541 0.518563 0.0403576 0 0 |***| 0 0 0 0 0 0.87391 0 0 0.000559029 0 |***| 0 0 0 -14.8919 0 -3.20976 
[2:36] Calculating q-values
[2:36] Number of IDs at 50%, 5%, 1%, 0.1% FDR: 3140, 1912, 1359, 1029
[2:36] Calculating q-values
[2:36] Number of IDs at 50%, 5%, 1%, 0.1% FDR: 3275, 2327, 1866, 1480
[2:36] Trying the other linear classifier
[2:36] Precursor search
[2:36] Optimising weights
Averages: 
0.206308 0.13092 0 0.135237 0.225574 0.305908 0 0 0 0 |***| 0 0 0 0 0 0 0 0 0.155324 0.165182 |***| 0.759575 0.0548033 0.862828 0.175773 -2.01852 0.0419616 0.158927 0.43321 0.273909 0 |***| 0.12025 0.0808293 0.0673816 0 0.0202782 0.170963 0 0 0.272697 0.190315 |***| 0.400989 0.303327 0.166952 -0.0381502 0 0.00497434 
Weights: 
0.0977524 0 0 1.42632 0.491876 0.37021 0 0 0 0 |***| 0 0 0 0 0 0 0 0 0.0663014 0 |***| 0 0 0 0 0 0.0318005 0.443587 0 0.0915877 0 |***| 0.130274 0 0 0 0.0595627 1.25138 0 0 0.1398 0.0659375 |***| 0.0829705 0 0.0390806 -7.43408 0 -1.6592 
[2:36] Calculating q-values
[2:36] Number of IDs at 50%, 5%, 1%, 0.1% FDR: 3146, 1923, 1413, 1029
[2:36] Calculating q-values
[2:36] Number of IDs at 50%, 5%, 1%, 0.1% FDR: 3258, 2230, 1686, 1404
[2:36] Switching back
[2:36] Reverting weights
[2:36] Precursor search
[2:36] Optimising weights
Averages: 
0.206308 0.13092 0 0.135237 0.225574 0.305908 0 0 0 0 |***| 0 0 0 0 0 0 0 0 0.155324 0.165182 |***| 0.759575 0.0548033 0.862828 0.175773 -2.01852 0.0419616 0.158927 0.43321 0.273909 0 |***| 0.12025 0.0808293 0.0673816 0 0.0202782 0.170963 0 0 0.272697 0.190315 |***| 0.400989 0.303327 0.166952 -0.0381502 0 0.00497434 
Weights: 
0.845594 0 0 2.79713 1.28793 0.522101 0 0 0 0 |***| 0 0 0 0 0 0 0 0 0.91682 0 |***| 0 0 0 0 0 0.0120016 0.771505 0.00519889 0 0 |***| 0 0 0 0 0 0.824676 0 0 0 0 |***| 0 0 0 -14.4818 0 -3.59004 
[2:37] Calculating q-values
[2:37] Number of IDs at 50%, 5%, 1%, 0.1% FDR: 3146, 1923, 1413, 1029
[2:37] Calculating q-values
[2:37] Number of IDs at 50%, 5%, 1%, 0.1% FDR: 3285, 2323, 1909, 1451
[2:37] Reverting weights
[2:37] Restoring classifier and weights to
0.0512043 0 0 1.11215 0.589881 0.500013 0 0 0 0 |***| 0 0 0 0 0 0 0 0 0 0 |***| 0 0 0 0 0 0.0441312 0.253502 0 0.0240761 0 |***| 0.015833 0 0 0 0.351188 1.4334 0 0 0.178485 0 |***| 0.0604436 0 0.0218223 -7.33573 0 -1.33642 
[2:37] Precursor search
[2:38] Optimising weights
Averages: 
0.20349 0.128164 0 0.132143 0.222282 0.305964 0 0 0 0 |***| 0 0 0 0 0 0 0 0 0.152496 0.162021 |***| 0.752836 0.0510119 0.83325 0.175453 -2.01786 -0.0112407 0.151876 0.424637 0.274603 0 |***| 0.119754 0.0790893 0.0645353 0.172685 0.0200507 0.167487 0.0425104 0.341747 0.271762 0.183195 |***| 0.394235 0.300038 0.165662 -0.0396685 0 0.00479403 
Weights: 
0.683015 0 0 2.49297 1.03168 0.417564 0 0 0 0 |***| 0 0 0 0 0 0 0 0 0.878337 0 |***| 0 0 0 0.210524 0 0 0.637926 0.0512608 0 0 |***| 0.315636 0 0 0.118518 0 0.956242 0 0 0 0 |***| 0 0 0 -15.9721 0 -3.45743 
[2:38] Calculating q-values
[2:38] Number of IDs at 50%, 5%, 1%, 0.1% FDR: 39224, 24368, 17559, 6760
[2:38] Calculating q-values
[2:38] Number of IDs at 50%, 5%, 1%, 0.1% FDR: 41925, 29355, 23620, 14519
[2:38] Removing low confidence identifications
[2:38] Removing interfering precursors
[2:39] Calculating q-values
[2:39] Number of IDs at 50%, 5%, 1%, 0.1% FDR: 36500, 27989, 22936, 14372
[2:39] Calibrating retention times
[2:39] 22936 precursors used for iRT estimation.
[2:40] Optimising weights
Averages: 
0.23489 0.147416 0 0.152494 0.258342 0.357957 0 0 0 0 |***| 0 0 0 0 0 0 0 0 0.175231 0.186096 |***| 0.868809 0.0577156 0.94266 0.20245 -2.32757 -0.0324225 0.175801 0.491292 0.319865 0 |***| 0.137882 0.0911021 0.0744468 0.201457 0.0216046 0.192284 0.0488514 0.395971 0.316403 0.212983 |***| 0.460596 0.350004 0.193062 -0.0450474 0 0.00512012 
Weights: 
0.572757 0 0 3.31546 1.55621 0.897808 0 0 0 0 |***| 0 0 0 0 0 0 0 0 0.757926 0 |***| 0 0 0 0 0 0 0.97384 0.0214004 0 0 |***| 0.138221 0 0 0.165271 0 1.48327 0 0 0 0 |***| 0 0 0 -21.2645 0 -4.42612 
[2:40] Training neural networks: 16139 targets, 17226 decoys
[2:42] Calculating q-values
[2:42] Number of IDs at 50%, 5%, 1%, 0.1% FDR: 38849, 29773, 25388, 18181
[2:42] Calibrating retention times
[2:42] 25388 precursors used for iRT estimation.
[2:42] Translating peaks within elution groups
[2:42] Calculating q-values
[2:42] Number of IDs at 50%, 5%, 1%, 0.1% FDR: 44122, 43223, 42067, 36501
[2:42] Scoring channels
[2:43] Precursors at 1% channel FDR: 27056 out of 37522
[2:43] Calculating protein q-values
[2:43] Number of genes identified at 1% FDR: 1854 (precursor-level), 1564 (protein-level) (inference performed using proteotypic peptides only)
[2:43] Quantification

[2:44] File #2/3
[2:44] Loading run H:\JD\eJD1445.raw
[2:47] Detected MS/MS range: 63.6705 - 2872.88
[2:48] Run loaded
[2:48] 76350 library precursors are potentially detectable
[2:48] Processing batch #1 out of 38 
[2:48] Precursor search
[2:49] Optimising weights
Averages: 
0.0643533 0.045026 0 0.0605871 0.125011 0.0760775 
Weights: 
2.40434 0 0 2.66795 1.29027 0.264778 
[2:49] Calculating q-values
[2:49] Number of IDs at 50%, 5%, 1%, 0.1% FDR: 679, 294, 224, 0
[2:49] Calibrating retention times
[2:49] 224 precursors used for iRT estimation.
[2:49] Precursor search
[2:49] Optimising weights
Averages: 
0.066761 0.0441259 0 0.0570294 0.120563 0.102161 
Weights: 
1.72873 0 0 3.45725 1.50305 0.413192 
[2:49] Calculating q-values
[2:49] Number of IDs at 50%, 5%, 1%, 0.1% FDR: 749, 314, 228, 0
[2:49] Calibrating retention times
[2:49] 228 precursors used for iRT estimation.
[2:49] Mass correction transform (309 precursors): 2.5029e-09 -0.000101901 -1.76492e-06 
[2:49] M/z SD: 3.35884 ppm
[2:49] Top 70% mass accuracy: 3.70599 ppm
[2:49] Top 70% mass accuracy without correction: 4.00804ppm
[2:49] MS1 mass correction transform (231 precursors): -2.49192e-10 -0.00173463 3.99584e-06 
[2:49] Top 70% MS1 mass accuracy: 2.19465 ppm
[2:49] Top 70% MS1 mass accuracy without correction: 2.41086ppm
[2:49] Recalibrating with mass accuracy 1.85299e-05, 1.09732e-05 (MS2, MS1)
[2:49] Processing batch #1 out of 38 
[2:49] Precursor search
[2:49] Optimising weights
Averages: 
0.0729957 0.0512256 0 0.0653639 0.135098 0.117873 
Weights: 
1.41544 0 0 3.87717 1.31605 0.597476 
[2:49] Calculating q-values
[2:49] Number of IDs at 50%, 5%, 1%, 0.1% FDR: 803, 370, 304, 0
[2:49] Calibrating retention times
[2:49] 304 precursors used for iRT estimation.
[2:49] Processing batch #2 out of 38 
[2:49] Precursor search
[2:50] Optimising weights
Averages: 
0.0787028 0.0453546 0 0.0594228 0.125983 0.153616 
Weights: 
1.7063 0 0 3.48742 1.37238 0.783046 
[2:50] Calculating q-values
[2:50] Number of IDs at 50%, 5%, 1%, 0.1% FDR: 1693, 798, 641, 0
[2:50] Calibrating retention times
[2:50] 641 precursors used for iRT estimation.
[2:50] Processing batch #3 out of 38 
[2:50] Precursor search
[2:51] Optimising weights
Averages: 
0.0794944 0.0486851 0 0.0621247 0.128945 0.148627 
Weights: 
1.58271 0 0 3.58297 1.40507 0.859171 
[2:51] Calculating q-values
[2:51] Number of IDs at 50%, 5%, 1%, 0.1% FDR: 2559, 1234, 942, 0
[2:51] Calibrating retention times
[2:51] 942 precursors used for iRT estimation.
[2:51] Precursor search
[2:51] Optimising weights
Averages: 
0.0796795 0.0493965 0 0.0629553 0.129131 0.144338 
Weights: 
1.5978 0 0 3.61226 1.39363 0.848689 
[2:51] Calculating q-values
[2:51] Number of IDs at 50%, 5%, 1%, 0.1% FDR: 2550, 1232, 943, 0
[2:51] Precursor search
[2:51] Optimising weights
Averages: 
0.0795889 0.0493917 0 0.0629323 0.129063 0.144709 
Weights: 
1.55244 0 0 3.66435 1.39064 0.853761 
[2:51] Calculating q-values
[2:51] Number of IDs at 50%, 5%, 1%, 0.1% FDR: 2550, 1231, 945, 0
[2:51] Precursor search
[2:51] Optimising weights
Averages: 
0.0797616 0.0491172 0 0.0626415 0.128905 0.145893 
Weights: 
1.61138 0 0 3.60497 1.38828 0.84919 
[2:51] Calculating q-values
[2:51] Number of IDs at 50%, 5%, 1%, 0.1% FDR: 2550, 1233, 943, 0
[2:51] Calibrating retention times
[2:51] 943 precursors used for iRT estimation.
[2:51] RT window set to 0.678952
[2:51] Mass correction transform (1220 precursors): 3.29535e-10 -0.00115812 1.41966e-06 
[2:51] M/z SD: 3.5289 ppm
[2:51] Top 70% mass accuracy: 4.06657 ppm
[2:51] Top 70% mass accuracy without correction: 4.31014ppm
[2:51] MS1 mass correction transform (957 precursors): -2.85207e-09 -0.00337679 8.33172e-06 
[2:51] Top 70% MS1 mass accuracy: 2.13778 ppm
[2:51] Top 70% MS1 mass accuracy without correction: 2.33318ppm
[2:51] Refining mass correction
[2:51] Calibrating retention times
[2:51] Mass correction transform (854 precursors): -1.15756e-09 -0.00150725 2.75084e-06 
[2:51] M/z SD: 3.55641 ppm
[2:51] Top 70% mass accuracy: 4.11919 ppm
[2:51] Top 70% mass accuracy without correction: 4.31014ppm
[2:51] MS1 mass correction transform (669 precursors): -1.40745e-09 -0.00248518 5.86117e-06 
[2:51] Top 70% MS1 mass accuracy: 2.14003 ppm
[2:51] Top 70% MS1 mass accuracy without correction: 2.33318ppm
[2:51] Recommended MS1 mass accuracy setting: 10.7002 ppm
[2:51] Using mass accuracy 1e-05, 5e-06 (MS2, MS1)
[2:51] Processing batch #1 out of 38 
[2:51] Precursor search
[2:51] Optimising weights
Averages: 
0.183042 0.107429 0 0.118043 0.201526 0.287956 
Weights: 
1.67943 0 0 3.48681 1.71542 1.15413 
[2:51] Calculating q-values
[2:51] Number of IDs at 50%, 5%, 1%, 0.1% FDR: 1020, 670, 531, 0
[2:51] Calibrating retention times
[2:51] 531 precursors used for iRT estimation.
[2:51] Processing batch #2 out of 38 
[2:51] Precursor search
[2:51] Optimising weights
Averages: 
0.185367 0.114433 0 0.122272 0.207711 0.279992 
Weights: 
1.68656 0 0 3.50906 1.70256 1.06205 
[2:51] Calculating q-values
[2:51] Number of IDs at 50%, 5%, 1%, 0.1% FDR: 2056, 1360, 1063, 0
[2:51] Calibrating retention times
[2:51] 1063 precursors used for iRT estimation.
[2:51] Processing batch #3 out of 38 
[2:51] Precursor search
[2:51] Optimising weights
Averages: 
0.191716 0.117957 0 0.124848 0.209756 0.294365 
Weights: 
1.78258 0 0 3.87148 1.57891 1.11136 
[2:51] Calculating q-values
[2:51] Number of IDs at 50%, 5%, 1%, 0.1% FDR: 3096, 2029, 1609, 1057
[2:51] Calibrating retention times
[2:51] 1609 precursors used for iRT estimation.
[2:51] Precursor search
[2:51] Optimising weights
Averages: 
0.191975 0.118715 0 0.125322 0.211037 0.292027 0 0 0 0 |***| 0 0 0 0 0 0 0 0 0.143304 0.156636 |***| 0.710632 0.0490344 0.812536 0.161926 -1.89612 0.161766 0.148575 0.400082 0.27117 0 |***| 0.112745 0.0726805 0.0611663 0 0.0176617 0.159316 0 0 0.257867 0.175722 |***| 0.395184 0.287008 0.162907 -0.0449524 0 0.00195895 
Weights: 
1e-09 0 0 1.86474 0.347171 0.373691 0 0 0 0 |***| 0 0 0 0 0 0 0 0 0 1.04657 |***| 0 0 0 0 0 0.0194702 0.429883 0 0.288098 0 |***| 0 0.305579 0 0 0 0 0 0 0.00187671 0 |***| 0.0576095 0 0.0245543 -8.32413 0 -1.51207 
[2:51] Calculating q-values
[2:51] Number of IDs at 50%, 5%, 1%, 0.1% FDR: 3246, 2197, 1841, 1180
[2:51] Calibrating retention times
[2:51] 1841 precursors used for iRT estimation.
[2:51] Precursor search
[2:51] Optimising weights
Averages: 
0.199501 0.125088 0 0.131006 0.222134 0.29598 0 0 0 0 |***| 0 0 0 0 0 0 0 0 0.150171 0.16249 |***| 0.748457 0.0519965 0.853117 0.175158 -1.99281 0.135054 0.151736 0.413449 0.265006 0 |***| 0.11757 0.0754004 0.0636408 0 0.02441 0.168134 0 0 0.258751 0.17494 |***| 0.387172 0.275855 0.152897 -0.0392299 0 0.00520406 
Weights: 
1e-09 0.360503 0 1.51297 0.437515 0.43247 0 0 0 0 |***| 0 0 0 0 0 0 0 0 0 0.421463 |***| 0 0 0 0 0 0.0309158 0.522841 0 0.235365 0 |***| 0 0.441544 0 0 0.167355 0.637911 0 0 0 0 |***| 0.0498645 0 0 -8.72327 0 -1.59187 
[2:51] Calculating q-values
[2:51] Number of IDs at 50%, 5%, 1%, 0.1% FDR: 3283, 2236, 1847, 1280
[2:51] Calibrating retention times
[2:51] 1847 precursors used for iRT estimation.
[2:51] Precursor search
[2:51] Optimising weights
Averages: 
0.198792 0.124939 0 0.130609 0.221528 0.295216 0 0 0 0 |***| 0 0 0 0 0 0 0 0 0.149589 0.162119 |***| 0.745162 0.0518804 0.857031 0.174484 -1.99138 0.136615 0.152413 0.413267 0.265244 0 |***| 0.117836 0.075041 0.0634067 0 0.0233813 0.1671 0 0 0.2594 0.17473 |***| 0.386629 0.277822 0.154237 -0.0397093 0 0.00549974 
Weights: 
1e-09 0.447995 0 1.41275 0.366988 0.390682 0 0 0 0 |***| 0 0 0 0 0 0 0 0 0 0.545755 |***| 0 0 0 0 0 0.0263456 0.590421 0 0.24455 0 |***| 0.146454 0.415783 0 0 0.114908 0.446531 0 0 0.00604921 0 |***| 0.0586138 0 0 -9.04149 0 -1.50394 
[2:51] Calculating q-values
[2:51] Number of IDs at 50%, 5%, 1%, 0.1% FDR: 3276, 2240, 1817, 1247
[2:51] Calibrating retention times
[2:51] 1817 precursors used for iRT estimation.
[2:51] Precursor search
[2:51] Optimising weights
Averages: 
0.199035 0.125021 0 0.130613 0.222 0.295931 0 0 0 0 |***| 0 0 0 0 0 0 0 0 0.149713 0.162144 |***| 0.744204 0.0517838 0.860187 0.175034 -1.9949 0.137358 0.150431 0.410342 0.265893 0 |***| 0.117141 0.0748415 0.0634411 0 0.0236207 0.167746 0 0 0.258855 0.173659 |***| 0.386074 0.276673 0.154383 -0.0394119 0 0.00493231 
Weights: 
1e-09 0.40514 0 1.43125 0.418399 0.396022 0 0 0 0 |***| 0 0 0 0 0 0 0 0 0 0.436131 |***| 0 0 0 0 0 0.0261653 0.498246 0 0.237867 0 |***| 0.092556 0.397653 0 0 0.169245 0.643567 0 0 0 0 |***| 0.0572389 0 0 -9.05079 0 -1.66247 
[2:51] Calculating q-values
[2:51] Number of IDs at 50%, 5%, 1%, 0.1% FDR: 3106, 1901, 1403, 0
[2:51] Calculating q-values
[2:51] Number of IDs at 50%, 5%, 1%, 0.1% FDR: 3281, 2238, 1824, 1257
[2:51] Trying the other linear classifier
[2:51] Precursor search
[2:51] Optimising weights
Averages: 
0.198477 0.124968 0 0.130497 0.221228 0.294791 0 0 0 0 |***| 0 0 0 0 0 0 0 0 0.149345 0.161811 |***| 0.744346 0.0517132 0.858395 0.174159 -1.98636 0.124554 0.152128 0.41283 0.265246 0 |***| 0.117513 0.0746724 0.0633001 0 0.0237392 0.166917 0 0 0.258366 0.174204 |***| 0.385684 0.276733 0.153601 -0.039621 0 0.00530483 
Weights: 
0.0527 0 0 2.509 1.33647 0.477883 0 0 0 0 |***| 0 0 0 0 0 0 0 0 1.0275 0 |***| 0 0 0 0 0 0.0212566 0.849055 0 0 0 |***| 0.440795 0 0 0 0 1.18018 0 0 0 0 |***| 0 0 0 -16.9865 0 -2.51004 
[2:51] Calculating q-values
[2:51] Number of IDs at 50%, 5%, 1%, 0.1% FDR: 3095, 1903, 1404, 0
[2:51] Calculating q-values
[2:51] Number of IDs at 50%, 5%, 1%, 0.1% FDR: 3302, 2339, 1882, 1288
[2:51] Calibrating retention times
[2:51] 1882 precursors used for iRT estimation.
[2:51] Restoring classifier and weights to
1e-09 0.360503 0 1.51297 0.437515 0.43247 0 0 0 0 |***| 0 0 0 0 0 0 0 0 0 0.421463 |***| 0 0 0 0 0 0.0309158 0.522841 0 0.235365 0 |***| 0 0.441544 0 0 0.167355 0.637911 0 0 0 0 |***| 0.0498645 0 0 -8.72327 0 -1.59187 
[2:51] Precursor search
[2:53] Optimising weights
Averages: 
0.200847 0.125915 0 0.130516 0.221319 0.305299 0 0 0 0 |***| 0 0 0 0 0 0 0 0 0.149964 0.161468 |***| 0.734243 0.0473731 0.816531 0.175307 -2.02913 0.050099 0.148548 0.413499 0.274748 0 |***| 0.117094 0.0748713 0.063162 0.170373 0.0192839 0.167406 0.0421369 0.342817 0.273532 0.183173 |***| 0.391489 0.293999 0.161142 -0.039665 0 0.00445868 
Weights: 
0.418389 0 0 2.58996 1.06204 0.436795 0 0 0 0 |***| 0 0 0 0 0 0 0 0 0.747617 0 |***| 0 0 0 0.283728 0 0 0.632201 0.0562556 0 0 |***| 0.228952 0 0 0.208123 0 1.09971 0 0 0 0 |***| 0 0 0 -16.0409 0 -3.09304 
[2:53] Calculating q-values
[2:53] Number of IDs at 50%, 5%, 1%, 0.1% FDR: 39422, 23774, 17234, 3224
[2:53] Calculating q-values
[2:53] Number of IDs at 50%, 5%, 1%, 0.1% FDR: 41949, 29285, 23456, 15364
[2:53] Removing low confidence identifications
[2:53] Removing interfering precursors
[2:54] Calculating q-values
[2:54] Number of IDs at 50%, 5%, 1%, 0.1% FDR: 36327, 27825, 22789, 15208
[2:54] Calibrating retention times
[2:54] 22789 precursors used for iRT estimation.
[2:54] Optimising weights
Averages: 
0.232716 0.145264 0 0.151067 0.25646 0.358779 0 0 0 0 |***| 0 0 0 0 0 0 0 0 0.17292 0.186102 |***| 0.851478 0.0539581 0.929008 0.201634 -2.34016 0.0378628 0.172124 0.480674 0.322021 0 |***| 0.13535 0.0862451 0.0730302 0.198745 0.0216952 0.192778 0.048935 0.398485 0.318661 0.213545 |***| 0.458327 0.343248 0.187972 -0.0452204 0 0.00447807 
Weights: 
0.316704 0 0 3.47571 1.64579 0.904941 0 0 0 0 |***| 0 0 0 0 0 0 0 0 0.628126 0 |***| 0 0 0 0 0 0 0.916632 0.0497808 0 0 |***| 0.00558944 0 0 0.269845 0 1.60415 0 0 0 0 |***| 0 0 0 -21.6213 0 -4.14337 
[2:55] Training neural networks: 16069 targets, 16927 decoys
[2:56] Calculating q-values
[2:56] Number of IDs at 50%, 5%, 1%, 0.1% FDR: 38570, 29697, 25314, 17694
[2:56] Calibrating retention times
[2:56] 25314 precursors used for iRT estimation.
[2:57] Translating peaks within elution groups
[2:57] Calculating q-values
[2:57] Number of IDs at 50%, 5%, 1%, 0.1% FDR: 43809, 42925, 41641, 35416
[2:57] Scoring channels
[2:58] Precursors at 1% channel FDR: 26562 out of 36958
[2:58] Calculating protein q-values
[2:58] Number of genes identified at 1% FDR: 1844 (precursor-level), 1513 (protein-level) (inference performed using proteotypic peptides only)
[2:58] Quantification

[2:59] File #3/3
[2:59] Loading run H:\JD\eJD1444.raw
[3:02] Detected MS/MS range: 63.6687 - 2874.27
[3:03] Run loaded
[3:03] 76350 library precursors are potentially detectable
[3:03] Processing batch #1 out of 38 
[3:03] Precursor search
[3:04] Optimising weights
Averages: 
0.0665082 0.0490693 0 0.0603659 0.133646 0.101381 
Weights: 
1.1563 0.127775 0 3.29118 1.42802 0.618241 
[3:04] Calculating q-values
[3:04] Number of IDs at 50%, 5%, 1%, 0.1% FDR: 770, 331, 182, 0
[3:04] Calibrating retention times
[3:04] 182 precursors used for iRT estimation.
[3:04] Precursor search
[3:04] Optimising weights
Averages: 
0.0698497 0.047756 0 0.05641 0.120512 0.125901 
Weights: 
0.456149 0.413168 0 3.92653 1.46814 0.900409 
[3:04] Calculating q-values
[3:04] Number of IDs at 50%, 5%, 1%, 0.1% FDR: 831, 341, 216, 0
[3:04] Calibrating retention times
[3:04] 216 precursors used for iRT estimation.
[3:04] Mass correction transform (338 precursors): -1.19e-09 -0.00146555 3.53696e-06 
[3:04] M/z SD: 3.27338 ppm
[3:04] Top 70% mass accuracy: 4.16178 ppm
[3:04] Top 70% mass accuracy without correction: 3.89312ppm
[3:04] No mass correction required
[3:04] MS1 mass correction transform (250 precursors): -1.79476e-09 -0.00305064 7.28352e-06 
[3:04] Top 70% MS1 mass accuracy: 2.21836 ppm
[3:04] Top 70% MS1 mass accuracy without correction: 2.65556ppm
[3:04] Recalibrating with mass accuracy 1.94656e-05, 1.10918e-05 (MS2, MS1)
[3:04] Processing batch #1 out of 38 
[3:04] Precursor search
[3:04] Optimising weights
Averages: 
0.0729259 0.0501467 0 0.0639874 0.139618 0.126079 
Weights: 
1.16325 0 0 3.65637 1.46053 0.703593 
[3:04] Calculating q-values
[3:04] Number of IDs at 50%, 5%, 1%, 0.1% FDR: 837, 372, 190, 0
[3:04] Calibrating retention times
[3:04] 190 precursors used for iRT estimation.
[3:04] Processing batch #2 out of 38 
[3:04] Precursor search
[3:05] Optimising weights
Averages: 
0.0778554 0.0481144 0 0.05917 0.129355 0.155822 
Weights: 
1.13434 0 0 3.84753 1.48208 0.932816 
[3:05] Calculating q-values
[3:05] Number of IDs at 50%, 5%, 1%, 0.1% FDR: 1709, 801, 534, 0
[3:05] Calibrating retention times
[3:05] 534 precursors used for iRT estimation.
[3:05] Processing batch #3 out of 38 
[3:05] Precursor search
[3:06] Optimising weights
Averages: 
0.0789345 0.0505139 0 0.0627466 0.129449 0.149203 
Weights: 
0.809789 0 0 4.42011 1.41204 1.04632 
[3:06] Calculating q-values
[3:06] Number of IDs at 50%, 5%, 1%, 0.1% FDR: 2574, 1221, 742, 0
[3:06] Calibrating retention times
[3:06] 742 precursors used for iRT estimation.
[3:06] Precursor search
[3:06] Optimising weights
Averages: 
0.0787666 0.0506434 0 0.0629561 0.131365 0.14573 
Weights: 
0.621713 0 0 4.59857 1.43156 1.05675 
[3:06] Calculating q-values
[3:06] Number of IDs at 50%, 5%, 1%, 0.1% FDR: 2566, 1224, 733, 0
[3:06] Precursor search
[3:06] Optimising weights
Averages: 
0.0789287 0.0506403 0 0.0627922 0.131019 0.146787 
Weights: 
0.573636 0 0 4.65398 1.4279 1.06272 
[3:06] Calculating q-values
[3:06] Number of IDs at 50%, 5%, 1%, 0.1% FDR: 2566, 1225, 734, 0
[3:06] Precursor search
[3:06] Optimising weights
Averages: 
0.0791213 0.0506087 0 0.0626773 0.130654 0.147673 
Weights: 
0.591599 0 0 4.64814 1.41665 1.06563 
[3:06] Calculating q-values
[3:06] Number of IDs at 50%, 5%, 1%, 0.1% FDR: 2575, 1225, 737, 0
[3:06] Calibrating retention times
[3:06] 737 precursors used for iRT estimation.
[3:06] RT window set to 0.67497
[3:06] Mass correction transform (1210 precursors): 2.46843e-10 -0.00100074 1.25664e-06 
[3:06] M/z SD: 3.44167 ppm
[3:06] Top 70% mass accuracy: 3.91246 ppm
[3:06] Top 70% mass accuracy without correction: 4.10874ppm
[3:06] MS1 mass correction transform (951 precursors): -4.54549e-10 -0.00221881 5.08841e-06 
[3:06] Top 70% MS1 mass accuracy: 2.1703 ppm
[3:06] Top 70% MS1 mass accuracy without correction: 2.53909ppm
[3:06] Refining mass correction
[3:06] Calibrating retention times
[3:06] Mass correction transform (847 precursors): 7.8152e-10 -0.000898819 5.2886e-07 
[3:06] M/z SD: 3.4572 ppm
[3:06] Top 70% mass accuracy: 3.93789 ppm
[3:06] Top 70% mass accuracy without correction: 4.10874ppm
[3:06] MS1 mass correction transform (665 precursors): -2.40442e-10 -0.00207668 4.64966e-06 
[3:06] Top 70% MS1 mass accuracy: 2.15864 ppm
[3:06] Top 70% MS1 mass accuracy without correction: 2.53909ppm
[3:06] Recommended MS1 mass accuracy setting: 10.7932 ppm
[3:06] Using mass accuracy 1e-05, 5e-06 (MS2, MS1)
[3:06] Processing batch #1 out of 38 
[3:06] Precursor search
[3:06] Optimising weights
Averages: 
0.187123 0.113528 0 0.121234 0.207478 0.299894 
Weights: 
1.18998 0 0 3.81389 1.75546 1.30284 
[3:06] Calculating q-values
[3:06] Number of IDs at 50%, 5%, 1%, 0.1% FDR: 1047, 670, 503, 0
[3:06] Calibrating retention times
[3:06] 503 precursors used for iRT estimation.
[3:06] Processing batch #2 out of 38 
[3:06] Precursor search
[3:06] Optimising weights
Averages: 
0.194542 0.11964 0 0.126415 0.21322 0.303551 
Weights: 
1.4818 0.36783 0 3.8882 1.47168 1.15562 
[3:06] Calculating q-values
[3:06] Number of IDs at 50%, 5%, 1%, 0.1% FDR: 2067, 1363, 1116, 0
[3:06] Calibrating retention times
[3:06] 1116 precursors used for iRT estimation.
[3:06] Processing batch #3 out of 38 
[3:06] Precursor search
[3:06] Optimising weights
Averages: 
0.198339 0.123476 0 0.126452 0.215823 0.319981 
Weights: 
1.23198 1.02679 0 3.44936 1.51768 1.33539 
[3:06] Calculating q-values
[3:06] Number of IDs at 50%, 5%, 1%, 0.1% FDR: 3107, 2101, 1627, 1183
[3:06] Calibrating retention times
[3:06] 1627 precursors used for iRT estimation.
[3:06] Precursor search
[3:06] Optimising weights
Averages: 
0.198853 0.124345 0 0.127558 0.21613 0.315287 0 0 0 0 |***| 0 0 0 0 0 0 0 0 0.146306 0.160483 |***| 0.723655 0.0494319 0.836959 0.167063 -1.90663 0.115173 0.150328 0.404938 0.284996 0 |***| 0.118452 0.0790086 0.0612032 0 0.0187059 0.165347 0 0 0.269327 0.176213 |***| 0.397643 0.302755 0.166048 -0.0451946 0 0.00161972 
Weights: 
1e-09 0 0 1.15535 0.556156 0.648093 0 0 0 0 |***| 0 0 0 0 0 0 0 0 0 0.498089 |***| 0 0 0.013151 0 0 0.00178415 0.236873 0 0 0 |***| 0.229143 0 0 0 0 0.89873 0 0 0 0 |***| 0 0.0135405 0.0228518 -7.83905 0 -0.846213 
[3:06] Calculating q-values
[3:06] Number of IDs at 50%, 5%, 1%, 0.1% FDR: 3259, 2275, 1880, 1254
[3:06] Calibrating retention times
[3:06] 1880 precursors used for iRT estimation.
[3:06] Precursor search
[3:06] Optimising weights
Averages: 
0.204452 0.130555 0 0.134327 0.227524 0.309384 0 0 0 0 |***| 0 0 0 0 0 0 0 0 0.152888 0.166954 |***| 0.753293 0.0523325 0.865314 0.180068 -2.0596 0.0783043 0.155873 0.420967 0.277097 0 |***| 0.122736 0.0809153 0.063468 0 0.0213047 0.171272 0 0 0.259786 0.168168 |***| 0.390892 0.295397 0.163428 -0.0397571 0 0.00317989 
Weights: 
0.0906645 0 0 0.928246 0.395168 0.59426 0 0 0 0 |***| 0 0 0 0 0 0 0 0 0 0.634846 |***| 0 0 0 0.171544 0 0 0.416627 0 0 0 |***| 0.476311 0 0 0 0 0.835066 0 0 0 0 |***| 0 0 0.0901118 -8.67727 0 -1.05332 
[3:06] Calculating q-values
[3:06] Number of IDs at 50%, 5%, 1%, 0.1% FDR: 3315, 2272, 1858, 1502
[3:06] Trying the other linear classifier
[3:06] Precursor search
[3:06] Optimising weights
Averages: 
0.198599 0.124022 0 0.127515 0.215693 0.31641 0 0 0 0 |***| 0 0 0 0 0 0 0 0 0.145864 0.160262 |***| 0.722132 0.0492592 0.833569 0.166395 -1.89913 0.118695 0.15069 0.405444 0.286074 0 |***| 0.118365 0.0790747 0.0611187 0 0.0183774 0.165138 0 0 0.269723 0.176274 |***| 0.397003 0.302404 0.16635 -0.0458351 0 0.00153759 
Weights: 
0.571597 0 0 2.69445 1.15841 0.569536 0 0 0 0 |***| 0 0 0 0 0 0 0 0 0.102925 0 |***| 0 0 0 0.0649039 0 0 0.452133 0 0 0 |***| 0.18131 0 0 0 0 1.4048 0 0 0 0 |***| 0 0 0 -15.7037 0 -2.39882 
[3:06] Calculating q-values
[3:06] Number of IDs at 50%, 5%, 1%, 0.1% FDR: 3266, 2298, 1910, 1359
[3:06] Calibrating retention times
[3:06] 1910 precursors used for iRT estimation.
[3:06] Precursor search
[3:06] Optimising weights
Averages: 
0.206293 0.129526 0 0.133059 0.225901 0.323105 0 0 0 0 |***| 0 0 0 0 0 0 0 0 0.152443 0.167168 |***| 0.749009 0.0520671 0.865552 0.178127 -2.02511 0.107714 0.154446 0.41636 0.289789 0 |***| 0.122523 0.0807562 0.0632432 0 0.02114 0.171313 0 0 0.272239 0.178365 |***| 0.398005 0.301516 0.16556 -0.0391643 0 0.00346055 
Weights: 
0.625868 0 0 2.7823 1.05075 0.519855 0 0 0 0 |***| 0 0 0 0 0 0 0 0 0.439275 0 |***| 0 0 0 0.368693 0 0 0.605815 0 0 0 |***| 0.237516 0 0 0 0 1.23738 0 0 0 0 |***| 0 0 0 -15.7774 0 -2.66989 
[3:06] Calculating q-values
[3:06] Number of IDs at 50%, 5%, 1%, 0.1% FDR: 3329, 2345, 1875, 1496
[3:06] Calibrating retention times
[3:06] 1875 precursors used for iRT estimation.
[3:06] Precursor search
[3:06] Optimising weights
Averages: 
0.205413 0.128547 0 0.132007 0.223781 0.324152 0 0 0 0 |***| 0 0 0 0 0 0 0 0 0.151387 0.166165 |***| 0.745341 0.0515381 0.862968 0.176172 -2.00058 0.102117 0.151908 0.410771 0.291344 0 |***| 0.122441 0.0806384 0.0632222 0 0.0210123 0.170335 0 0 0.273327 0.178906 |***| 0.399407 0.302097 0.166487 -0.040377 0 0.00367931 
Weights: 
0.703513 0 0 2.64836 1.03102 0.488402 0 0 0 0 |***| 0 0 0 0 0 0 0 0 0.33845 0 |***| 0 0 0 0.386046 0 0 0.606824 0 0 0 |***| 0.338481 0 0 0 0 1.19508 0 0 0 0 |***| 0 0 0 -16.1744 0 -2.46679 
[3:06] Calculating q-values
[3:06] Number of IDs at 50%, 5%, 1%, 0.1% FDR: 3126, 1890, 1415, 0
[3:06] Calculating q-values
[3:06] Number of IDs at 50%, 5%, 1%, 0.1% FDR: 3328, 2331, 1915, 1517
[3:06] Trying the other linear classifier
[3:06] Precursor search
[3:06] Optimising weights
Averages: 
0.205819 0.129202 0 0.132492 0.225354 0.321907 0 0 0 0 |***| 0 0 0 0 0 0 0 0 0.152168 0.166751 |***| 0.746712 0.052139 0.864605 0.17773 -2.02039 0.107081 0.154137 0.415169 0.289273 0 |***| 0.122592 0.080594 0.0632488 0 0.0209464 0.170936 0 0 0.27127 0.177733 |***| 0.39682 0.300172 0.165406 -0.0400201 0 0.00338749 
Weights: 
0.128537 0 0 0.785693 0.487552 0.574941 0 0 0 0 |***| 0 0 0 0 0 0 0 0 0 0.893143 |***| 0 0 0 0.00471392 0 0.000710805 0.404581 0 0.0535183 0 |***| 0.483948 0 0 0 0 0.631615 0 0 0 0 |***| 0 0 0.0840965 -8.84866 0 -1.18793 
[3:06] Calculating q-values
[3:07] Number of IDs at 50%, 5%, 1%, 0.1% FDR: 3137, 1898, 1412, 0
[3:07] Calculating q-values
[3:07] Number of IDs at 50%, 5%, 1%, 0.1% FDR: 3305, 2269, 1783, 1555
[3:07] Switching back
[3:07] Reverting weights
[3:07] Precursor search
[3:07] Optimising weights
Averages: 
0.205819 0.129202 0 0.132492 0.225354 0.321907 0 0 0 0 |***| 0 0 0 0 0 0 0 0 0.152168 0.166751 |***| 0.746712 0.052139 0.864605 0.17773 -2.02039 0.107081 0.154137 0.415169 0.289273 0 |***| 0.122592 0.080594 0.0632488 0 0.0209464 0.170936 0 0 0.27127 0.177733 |***| 0.39682 0.300172 0.165406 -0.0400201 0 0.00338749 
Weights: 
0.645152 0 0 2.61451 1.06173 0.482935 0 0 0 0 |***| 0 0 0 0 0 0 0 0 0.410375 0 |***| 0 0 0 0.344361 0 0 0.639822 0 0 0 |***| 0.300838 0 0 0 0 1.27666 0 0 0 0 |***| 0 0 0 -16.0331 0 -2.69998 
[3:07] Calculating q-values
[3:07] Number of IDs at 50%, 5%, 1%, 0.1% FDR: 3137, 1898, 1412, 0
[3:07] Calculating q-values
[3:07] Number of IDs at 50%, 5%, 1%, 0.1% FDR: 3332, 2344, 1903, 1534
[3:07] Reverting weights
[3:07] Restoring classifier and weights to
0.571597 0 0 2.69445 1.15841 0.569536 0 0 0 0 |***| 0 0 0 0 0 0 0 0 0.102925 0 |***| 0 0 0 0.0649039 0 0 0.452133 0 0 0 |***| 0.18131 0 0 0 0 1.4048 0 0 0 0 |***| 0 0 0 -15.7037 0 -2.39882 
[3:07] Precursor search
[3:08] Optimising weights
Averages: 
0.204284 0.126912 0 0.131328 0.220712 0.323204 0 0 0 0 |***| 0 0 0 0 0 0 0 0 0.150417 0.163487 |***| 0.739557 0.0499432 0.824563 0.174355 -1.99095 0.141717 0.149945 0.417476 0.294351 0 |***| 0.11847 0.0767114 0.0631372 0.174057 0.0201334 0.168743 0.0418454 0.342084 0.279694 0.187686 |***| 0.402231 0.302671 0.166346 -0.0400289 0 0.00404403 
Weights: 
0.505104 0 0 2.5938 0.880932 0.574923 0 0 0 0 |***| 0 0 0 0 0 0 0 0 0.599507 0 |***| 0 0 0 0.309897 0 0 0.621435 0.047977 0 0 |***| 0.36178 0 0 0.024381 0 1.19238 0 0 0 0 |***| 0 0 0 -15.9958 0 -3.12861 
[3:09] Calculating q-values
[3:09] Number of IDs at 50%, 5%, 1%, 0.1% FDR: 39521, 24332, 17427, 5560
[3:09] Calculating q-values
[3:09] Number of IDs at 50%, 5%, 1%, 0.1% FDR: 41954, 29488, 24010, 14662
[3:09] Removing low confidence identifications
[3:09] Removing interfering precursors
[3:09] Calculating q-values
[3:09] Number of IDs at 50%, 5%, 1%, 0.1% FDR: 36419, 28163, 23400, 14523
[3:09] Calibrating retention times
[3:09] 23400 precursors used for iRT estimation.
[3:10] Optimising weights
Averages: 
0.236265 0.146016 0 0.151746 0.256716 0.378696 0 0 0 0 |***| 0 0 0 0 0 0 0 0 0.173149 0.188131 |***| 0.854228 0.056751 0.932032 0.201201 -2.30493 0.143679 0.1734 0.48321 0.343669 0 |***| 0.136613 0.0887405 0.0730315 0.204111 0.0223364 0.194091 0.0482956 0.39546 0.327144 0.219144 |***| 0.470086 0.353243 0.194055 -0.0456662 0 0.00389741 
Weights: 
0.365072 0 0 3.41863 1.46076 1.07555 0 0 0 0 |***| 0 0 0 0 0 0 0 0 0.455342 0 |***| 0 0 0 0 0 0 0.95864 0.019504 0 0 |***| 0.151682 0 0 0.0767853 0 1.83168 0 0 0 0 |***| 0 0 0 -21.4421 0 -4.16856 
[3:10] Training neural networks: 16107 targets, 16998 decoys
[3:12] Calculating q-values
[3:12] Number of IDs at 50%, 5%, 1%, 0.1% FDR: 38784, 29671, 25644, 17483
[3:12] Calibrating retention times
[3:12] 25644 precursors used for iRT estimation.
[3:12] Translating peaks within elution groups
[3:12] Calculating q-values
[3:12] Number of IDs at 50%, 5%, 1%, 0.1% FDR: 43910, 42989, 41999, 35348
[3:12] Scoring channels
[3:13] Precursors at 1% channel FDR: 27355 out of 37341
[3:13] Calculating protein q-values
[3:13] Number of genes identified at 1% FDR: 1854 (precursor-level), 1484 (protein-level) (inference performed using proteotypic peptides only)
[3:13] Quantification

[3:14] Cross-run analysis
[3:14] Reading quantification information: 3 files
[3:14] Quantifying peptides
[3:14] Quantifying proteins
[3:14] Calculating q-values for protein and gene groups
[3:14] Calculating global q-values for protein and gene groups
[3:14] Writing report
[3:25] Report saved to H:\JD\eJD1443_45\Report.tsv.
[3:25] Saving precursor levels matrix
[3:25] Precursor levels matrix (1% precursor and protein group FDR) saved to H:\JD\eJD1443_45\Report.pr_matrix.tsv.
[3:25] Saving precursor levels matrix
[3:26] Precursor levels matrix (1% precursor and protein group FDR) saved to H:\JD\eJD1443_45\Report.pr_matrix_channels.tsv.
[3:26] Saving precursor levels matrix
[3:26] Precursor levels matrix (1% precursor and protein group FDR) saved to H:\JD\eJD1443_45\Report.pr_matrix_channels_translated.tsv.
[3:26] Saving precursor levels matrix
[3:26] Precursor levels matrix (1% precursor and protein group FDR) saved to H:\JD\eJD1443_45\Report.pr_matrix_channels_ms1.tsv.
[3:26] Saving precursor levels matrix
[3:26] Precursor levels matrix (1% precursor and protein group FDR) saved to H:\JD\eJD1443_45\Report.pr_matrix_channels_ms1_translated.tsv.
[3:26] Saving precursor levels matrix
[3:26] Precursor levels matrix (1% precursor and protein group FDR) saved to H:\JD\eJD1443_45\Report.pr_matrix_channels_ms1_extracted.tsv.
[3:26] Saving protein group levels matrix
[3:26] Protein group levels matrix (1% precursor FDR and protein group FDR) saved to H:\JD\eJD1443_45\Report.pg_matrix.tsv.
[3:26] Saving gene group levels matrix
[3:26] Gene groups levels matrix (1% precursor FDR and protein group FDR) saved to H:\JD\eJD1443_45\Report.gg_matrix.tsv.
[3:26] Saving unique genes levels matrix
[3:26] Unique genes levels matrix (1% precursor FDR and protein group FDR) saved to H:\JD\eJD1443_45\Report.unique_genes_matrix.tsv.
[3:26] Stats report saved to H:\JD\eJD1443_45\Report.stats.tsv

Finished