DIA-NN exits during precursor search

Hi Vadim,
I ran a library-free search on diaPASEF data on my PC, but DIA-NN exits during precursor search, without writing any report. What might be causing this issue?
Thanks so much!
diann.exe --f "Z:\Projects\Internal\DIA\Data\200925_500ng_SY11_iRT_dia_Slot1-16_1_1055.d
" --lib "" --threads 2 --verbose 5 --out "C:\DIA-NN\1.8\report.tsv" --qvalue 0.01 --matrices  --out-lib "C:\DIA-NN\1.8\report-lib.tsv" --gen-spec-lib --predictor --fasta "P:\Others\db\2021-02-26-decoys-human_uniprot_contam-trembl-plus-swissprot.fasta.fas" --fasta-search --min-fr-mz 200 --max-fr-mz 1800 --met-excision --cut K*,R* --missed-cleavages 1 --min-pep-len 7 --max-pep-len 30 --min-pr-mz 300 --max-pr-mz 1800 --min-pr-charge 1 --max-pr-charge 4 --unimod4 --var-mods 1 --var-mod UniMod:35,15.994915,M --var-mod UniMod:1,42.010565,*n --monitor-mod UniMod:1 --smart-profiling --no-ifs-removal 
DIA-NN 1.8 (Data-Independent Acquisition by Neural Networks)
Compiled on Jun 28 2021 14:55:31
Current date and time: Wed Aug  4 09:45:50 2021
CPU: GenuineIntel Intel(R) Core(TM) i5-8500 CPU @ 3.00GHz
SIMD instructions: AVX AVX2 FMA SSE4.1 SSE4.2 
Logical CPU cores: 6
Thread number set to 2
Output will be filtered at 0.01 FDR
Precursor/protein x samples expression level matrices will be saved along with the main report
A spectral library will be generated
Deep learning will be used to generate a new in silico spectral library from peptides provided
Library-free search enabled
Min fragment m/z set to 200
Max fragment m/z set to 1800
N-terminal methionine excision enabled
In silico digest will involve cuts at K*,R*
Maximum number of missed cleavages set to 1
Min peptide length set to 7
Max peptide length set to 30
Min precursor m/z set to 300
Max precursor m/z set to 1800
Min precursor charge set to 1
Max precursor charge set to 4
Cysteine carbamidomethylation enabled as a fixed modification
Maximum number of variable modifications set to 1
Modification UniMod:35 with mass delta 15.9949 at M will be considered as variable
Modification UniMod:1 with mass delta 42.0106 at *n will be considered as variable
When generating a spectral library, in silico predicted spectra will be retained if deemed more reliable than experimental ones
Interference removal from fragment elution curves disabled
DIA-NN will optimise the mass accuracy automatically using the first run in the experiment. This is useful primarily for quick initial analyses, when it is not yet known which mass accuracy setting works best for a particular acquisition scheme.
Exclusion of fragments shared between heavy and light peptides from quantification is not supported in library-free mode - disabled
The following variable modifications will be scored: UniMod:1 

1 files will be processed
[0:00] Loading FASTA P:\Others\db\2021-02-26-decoys-human_uniprot_contam-trembl-plus-swissprot.fasta.fas
WARNING: 59 sequences skipped due to duplicate protein ids; use --duplicate-proteins to disable skipping duplicates
[0:18] Processing FASTA
[0:42] Assembling elution groups
[1:16] 13429267 precursors generated
[1:16] Protein names missing for some isoforms
[1:16] Gene names missing for some isoforms
[1:16] Library contains 80552 proteins, and 26079 genes
[1:18] Encoding peptides for spectra and RTs prediction
[1:57] Predicting spectra and IMs
Predictions generated:
4864
9728
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7064452
7069316
7074180
7079044
7083908
7088772
7093636
7098500
7103364
7108228
7113092
7117956
7122820
7127684
7132548
7137412
7142276
7147140
7152004
7156868
7161817
7166681
7171545
7176409
7181273
7186137
7191001
7195865
7200729
7205593
7210457
7215321
7220185
7225049
7229913
7234777
7239641
7244505
7249369
7254233
7259097
7263961
7268825
7273689
7278553
7283417
7288281
7293145
7298009
7302873
7307737
7312601
7317465
7322329
7327193
7332057
7336921
7341785
7346649
7351513
7356377
7361241
7366105
7370969
7375833
7380697
7385561
7390425
7395289
7400153
7405017
7409881
7414745
7419609
7424473
7429337
7434201
7439065
7443929
7448793
7453657
7458521
7463385
7468249
7473113
7477977
7482841
7487705
7492569
7497433
7502297
7507161
7512025
7516889
7521753
7526617
7531481
7536345
7541209
7546073
7550937
7555801
7560665
7565529
7570393
7575257
7580121
7584985
7589849
7594713
7599577
7604441
7609305
7614169
7619033
7623897
7628761
7633625
7638489
7643353
7648217
7653081
7657945
7662809
7667673
7672537
7677401
7682265
7687129
7691993
7696857
7701721
7706585
7711449
7716313
7721177
7726041
7730905
7735769
7740633
7745497
7750361
7755225
7760089
7764953
7769817
7774681
7779545
7784409
7789273
7794137
7799001
7803865
7808729
7813593
7818457
7823321
7828185
7833049
7837913
7842777
7847641
7852505
7857369
7862233
7867097
7871961
7876825
7881689
7886553
7891417
7896281
7901145
7906009
7910873
7915737
7920601
7925465
7930329
7935193
7940057
7944921
7949785
7954649
7959513
7964377
7969241
7974105
7978969
7983833
7988697
7993561
7998425
8003289
8008153
8013017
8017881
8022745
8027609
8032473
8037337
8042201
8047065
8051929
8056793
8061657
8066521
8071385
8076249
8081196
8086060
8090924
8095788
8100652
8105516
8110380
8115244
8120108
8124972
8129836
8134700
8139564
8144428
8149292
8154156
8159020
8163884
8168748
8173612
8178476
8183340
8188204
8193068
8197932
8202796
8207660
8212524
8217388
8222252
8227116
8231980
8236844
8241708
8246572
8251436
8256300
8261164
8266028
8270892
8275756
8280620
8285484
8290348
8295212
8300076
8304940
8309804
8314668
8319532
8324396
8329260
8334124
8338988
8343852
8348716
8353580
8358444
8363308
8368172
8373036
8377900
8382764
8387628
8392492
8397356
8402220
8407084
8411948
8416812
8421676
8426540
8431404
8436268
8441132
8445996
8450860
8455724
8460588
8465452
8470316
8475180
8480044
8484908
8489772
8494636
8499500
8504364
8509228
8514092
8518956
8523820
8528684
8533548
8538412
8543276
8548140
8553004
8557868
8562732
8567596
8572460
8577324
8582188
8587052
8591916
8596780
8601644
8606508
8611372
8616236
8621100
8625964
8630828
8635692
8640556
8645420
8650284
8655148
8660012
8664876
8669740
8674604
8679468
8684332
8689196
8694060
8698924
8703788
8708652
8713516
8718380
8723244
8728108
8732972
8737836
8742700
8747564
8752428
8757292
8762156
8767020
8771884
8776748
8781612
8786476
8791340
8796204
8801068
8805932
8810796
8815660
8820524
8825388
8830252
8835116
8839980
8844844
8849708
8854572
8859436
8864300
8869164
8874028
8878892
8883756
8888620
8893484
8898348
8903212
8908076
8912940
8917804
8922668
8927532
8932396
8937260
8942124
8946988
8951852
8956716
8961580
8966444
8971308
8976172
8981036
8985900
8990764
8995628
9000492
9005356
9010220
9015084
9019948
9024812
9029676
9034540
9039404
9044430
9049294
9054158
9059022
9063886
9068750
9073614
9078478
9083342
9088206
9093070
9097934
9102798
9107662
9112526
9117390
9122254
9127118
9131982
9136846
9141710
9146574
9151438
9156302
9161166
9166030
9170894
9175758
9180622
9185486
9190350
9195214
9200078
9204942
9209806
9214670
9219534
9224398
9229262
9234126
9238990
9243854
9248718
9253582
9258446
9263310
9268174
9273038
9277902
9282766
9287630
9292494
9297358
9302222
9307086
9311950
9316814
9321678
9326542
9331406
9336270
9341134
9345998
9350862
9355726
9360590
9365454
9370318
9375182
9380046
9384910
9389774
9394638
9399502
9404366
9409230
9414094
9418958
9423822
9428686
9433550
9438414
9443278
9448142
9453006
9457870
9462734
9467598
9472462
9477326
9482190
9487054
9491918
9496782
9501646
9506510
9511374
9516238
9521102
9525966
9530830
9535694
9540558
9545422
9550286
9555150
9560014
9564878
9569742
9574606
9579470
9584334
9589198
9594062
9598926
9603790
9608654
9613518
9618382
9623246
9628110
9632974
9637838
9642702
9647566
9652430
9657294
9662158
9667022
9671886
9676750
9681614
9686478
9691342
9696206
9701070
9705934
9710798
9715662
9720526
9725390
9730254
9735118
9739982
9744846
9749710
9754574
9759438
9764302
9769166
9774030
9778894
9783758
9788622
9793486
9798350
9803214
9808078
9812942
9817806
9822670
9827534
9832398
9837262
9842126
9846990
9851854
9856718
9861582
9866446
9871310
9876174
9881038
9885902
9890766
9895630
9900494
9905358
9910222
9915086
9919950
9924814
9929678
9934542
9939406
9944270
9949134
9953998
9958862
9963726
9968590
9973454
9978318
9983182
9988046
9992910
9997774
10002638
10007502
10012366
10017230
10022094
10026981
10031845
10036709
10041573
10046437
10051301
10056165
10061029
10065893
10070757
10075621
10080485
10085349
10090213
10095077
10099941
10104805
10109669
10114533
10119397
10124261
10129125
10133989
10138853
10143717
10148581
10153445
10158309
10163173
10168037
10172901
10177765
10182629
10187493
10192357
10197221
10202085
10206949
10211813
10216677
10221541
10226405
10231269
10236133
10240997
10245861
10250725
10255589
10260453
10265317
10270181
10275045
10279909
10284773
10289637
10294501
10299365
10304229
10309093
10313957
10318821
10323685
10328549
10333413
10338277
10343141
10348005
10352869
10357733
10362597
10367461
10372325
10377189
10382053
10386917
10391781
10396645
10401509
10406373
10411237
10416101
10420965
10425829
10430693
10435557
10440421
10445285
10450149
10455013
10459877
10464741
10469605
10474469
10479333
10484197
10489061
10493925
10498789
10503653
10508517
10513381
10518245
10523109
10527973
10532837
10537701
10542565
10547429
10552293
10557157
10562021
10566885
10571749
10576613
10581477
10586341
10591205
10596069
10600933
10605797
10610661
10615525
10620389
10625253
10630117
10634981
10639845
10644709
10649573
10654437
10659301
10664165
10669029
10673893
10678757
10683621
10688485
10693349
10698213
10703077
10707941
10712805
10717669
10722533
10727397
10732261
10737125
10741989
10746853
10751717
10756581
10761445
10766309
10771173
10776037
10780901
10785765
10790629
10795493
10800357
10805221
10810085
10814949
10819813
10824677
10829541
10834405
10839269
10844133
10848997
10853861
10858725
10863589
10868453
10873317
10878181
10883045
10887909
10892773
10897637
10902501
10907365
10912229
10917093
10921957
10926821
10931685
10936549
10941413
10946277
10951141
10956005
10961098
10965962
10970826
10975690
10980554
10985418
10990282
10995146
11000010
11004874
11009738
11014602
11019466
11024330
11029194
11034058
11038922
11043786
11048650
11053514
11058378
11063242
11068106
11072970
11077834
11082698
11087562
11092426
11097290
11102154
11107018
11111882
11116746
11121610
11126474
11131338
11136202
11141066
11145930
11150794
11155658
11160522
11165386
11170250
11175114
11179978
11184842
11189706
11194570
11199434
11204298
11209162
11214026
11218890
11223754
11228618
11233482
11238346
11243210
11248074
11252938
11257802
11262666
11267530
11272394
11277258
11282122
11286986
11291850
11296714
11301578
11306442
11311306
11316170
11321034
11325898
11330762
11335626
11340490
11345354
11350218
11355082
11359946
11364810
11369674
11374538
11379402
11384266
11389130
11393994
11398858
11403722
11408586
11413450
11418314
11423178
11428042
11432906
11437770
11442634
11447498
11452362
11457226
11462090
11466954
11471818
11476682
11481546
11486410
11491274
11496138
11501002
11505866
11510730
11515594
11520458
11525322
11530186
11535050
11539914
11544778
11549642
11554506
11559370
11564234
11569098
11573962
11578826
11583690
11588554
11593418
11598282
11603146
11608010
11612874
11617738
11622602
11627466
11632330
11637194
11642058
11646922
11651786
11656650
11661514
11666378
11671242
11676106
11680970
11685834
11690698
11695562
11700426
11705290
11710154
11715018
11719882
11724746
11729610
11734474
11739338
11744202
11749066
11753930
11758794
11763658
11768522
11773386
11778250
11783114
11787978
11792842
11797706
11802570
11807434
11812298
11817162
11822026
11826890
11831754
11836618
11841482
11846346
11851210
11856074
11860938
11865829
11870693
11875557
11880421
11885285
11890149
11895013
11899877
11904741
11909605
11914469
11919333
11924197
11929061
11933925
11938789
11943653
11948517
11953381
11958245
11963109
11967973
11972837
11977701
11982565
11987429
11992293
11997157
12002021
12006885
12011749
12016613
12021477
12026341
12031205
12036069
12040933
12045797
12050661
12055525
12060389
12065253
12070117
12074981
12079845
12084709
12089573
12094437
12099301
12104165
12109029
12113893
12118757
12123621
12128485
12133349
12138213
12143077
12147941
12152805
12157669
12162533
12167397
12172261
12177125
12181989
12186853
12191717
12196581
12201445
12206309
12211173
12216037
12220901
12225765
12230629
12235493
12240357
12245221
12250085
12254949
12259813
12264677
12269541
12274405
12279269
12284133
12288997
12293861
12298725
12303589
12308453
12313317
12318181
12323045
12327909
12332773
12337637
12342501
12347365
12352229
12357093
12361957
12366821
12371685
12376549
12381413
12386277
12391141
12396005
12400869
12405733
12410597
12415461
12420325
12425189
12430053
12434917
12439781
12444645
12449509
12454373
12459237
12464101
12468965
12473829
12478693
12483557
12488421
12493285
12498149
12503013
12507877
12512741
12517605
12522469
12527333
12532197
12537061
12541925
12546789
12551653
12556517
12561381
12566245
12571109
12575973
12580837
12585701
12590565
12595429
12600293
12605157
12610021
12614885
12619749
12624613
12629477
12634341
12639205
12644069
12648933
12653797
12658661
12663525
12668389
12673253
12678117
12682981
12687845
12692709
12697573
12702437
12707385
12712249
12717113
12721977
12726841
12731705
12736569
12741433
12746297
12751161
12756025
12760889
12765753
12770617
12775481
12780345
12785209
12790073
12794937
12799801
12804665
12809529
12814393
12819257
12824121
12828985
12833849
12838713
12843577
12848441
12853305
12858169
12863033
12867897
12872761
12877625
12882489
12887353
12892217
12897081
12901945
12906809
12911673
12916537
12921401
12926265
12931129
12935993
12940857
12945721
12950585
12955449
12960313
12965177
12970041
12974905
12979769
12984633
12989497
12994361
12999225
13004089
13008953
13013817
13018681
13023545
13028409
13033273
13038137
13043001
13047865
13052729
13057593
13062457
13067321
13072185
13077049
13081913
13086777
13091641
13096505
13101369
13106233
13111097
13115961
13120825
13125689
13130553
13135417
13140281
13145145
13150009
13154873
13159737
13164601
13169465
13174329
13179193
13184057
13188921
13193785
13198649
13203513
13208377
13213241
13218105
13222969
13227833
13232697
13237561
13242425
13247289
13252153
13257017
13261881
13266745
13271609
13276473
13281337
13286201
13291065
13295929
13300793
13305657
13310521
13315385
13320249
13325113
13329977
13334841
13339705
13344569
13349433
13354297
13359161
13364025
13368889
13373753
13378617
13383481
13388345
13393209
13398073
13402937
13407801
13412665
13417529
13422393
13427257
[254:44] Predicting RTs
Predictions generated:
4864
9728
14592
19456
24320
29184
34048
38912
43776
48640
53504
58368
63232
68096
72960
78063
82927
87791
92655
97519
102383
107247
112111
116975
121839
126703
131567
136431
141295
146159
151023
156045
160909
165773
170637
175501
180365
185229
190093
194957
199821
204685
209549
214413
219277
224141
229005
233869
238956
243820
248684
253548
258412
263276
268140
273004
277868
282732
287596
292460
297324
302188
307052
311916
316780
321644
326508
331392
336256
341120
345984
350848
355712
360576
365440
370304
375168
380032
384896
389760
394624
399488
404352
409216
414080
418944
423808
428817
433681
438545
443409
448273
453137
458001
462865
467729
472593
477457
482321
487185
492049
496913
501777
506641
511505
516369
521233
526097
530965
535829
540693
545557
550421
555285
560149
565013
569877
574741
579605
584469
589333
594197
599061
603925
608789
613653
618517
623381
628245
633109
637973
642851
647715
652579
657443
662307
667171
672035
676899
681763
686627
691491
696355
701219
706083
710947
715811
720675
725539
730403
735267
740131
744995
749859
754723
759788
764652
769516
774380
779244
784108
788972
793836
798700
803564
808428
813292
818156
823020
827884
832748
837612
842476
847340
852204
857068
861932
866796
871660
876524
881452
886316
891180
896044
900908
905772
910636
915500
920364
925228
930092
934956
939820
944684
949548
954412
959276
964140
969004
973868
978732
983596
988460
993324
998188
1003052
1007916
1012970
1017834
1022698
1027562
1032426
1037290
1042154
1047018
1051882
1056746
1061610
1066474
1071338
1076202
1081066
1085930
1090794
1095658
1100522
1105386
1110250
1115114
1119978
1124842
1129706
1134570
1139434
1144298
1149162
1154056
1158920
1163784
1168648
1173512
1178376
1183240
1188104
1192968
1197832
1202696
1207560
1212424
1217288
1222152
1227016
1231880
1236744
1241608
1246472
1251336
1256200
1261064
1265928
1270792
1275656
1280520
1285384
1290248
1295112
1299976
1304840
1309738
1314602
1319466
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[300:53] Decoding predicted spectra and IMs
[303:22] Decoding RTs
[304:06] Saving the library to C:\DIA-NN\1.8\report-lib.predicted.speclib
[305:42] Initialising library

[305:49] File #1/1
[305:49] Loading run Z:\Projects\Internal\DIA\Data\200925_500ng_SY11_iRT_dia_Slot1-16_1_1055.d
For most diaPASEF datasets it is better to manually fix both the MS1 and MS2 mass accuracies to 10 ppm.
[456:23] Detected MS/MS range: 94.9951 - 1704.99
[456:57] Run loaded
[457:39] 9492840 library precursors are potentially detectable
[457:40] Processing batch #1 out of 4746 
[457:40] Precursor search
[458:53] Optimising weights
Ids at 10% FDR using TC scoring: 20
Ids at 10% FDR using TC selection: 20
Averages: 
0.0190895 0.00275754 0 0.0365746 -0.00273564 0.00549333 
Weights: 
4.45087 0 0 0 0.0499016 0 
[458:54] Calculating q-values
[458:54] Number of IDs at 50%, 5%, 1%, 0.1% FDR: 94, 23, 0, 0
[458:54] Calibrating retention times
[458:54] 50 precursors used for iRT estimation.
[458:54] Processing batch #2 out of 4746 
[458:54] Precursor search
[459:09] Optimising weights
Ids at 10% FDR using TC scoring: 28
Ids at 10% FDR using TC selection: 28
Averages: 
0.0071958 0.00246774 0 0.00777897 0.0449806 0.0533402 
Weights: 
3.24849 1.18638 0 0 0.608905 0.18723 
[459:09] Calculating q-values
[459:09] Number of IDs at 50%, 5%, 1%, 0.1% FDR: 192, 34, 0, 0
[459:09] Calibrating retention times
[459:09] 50 precursors used for iRT estimation.
[459:09] Processing batch #3 out of 4746 
[459:09] Precursor search
[459:25] Optimising weights
Ids at 10% FDR using TC scoring: 40
Ids at 10% FDR using TC selection: 40
Averages: 
0.0151037 -0.00821106 0 -0.00300138 0.0831956 0.110026 
Weights: 
1.01244 2.31135 0 0 0.782526 0.882325 
[459:25] Calculating q-values
[459:25] Number of IDs at 50%, 5%, 1%, 0.1% FDR: 224, 66, 0, 0
[459:25] Calibrating retention times
[459:25] 50 precursors used for iRT estimation.
[459:25] Processing batch #4 out of 4746 
[459:25] Precursor search
[459:40] Optimising weights
Ids at 10% FDR using TC scoring: 49
Ids at 10% FDR using TC selection: 49
Averages: 
0.0135639 -0.0128547 0 -0.0193773 0.0460159 0.12249 
Weights: 
2.22504 1.91324 0 0 0.795491 0.743059 
[459:40] Calculating q-values
[459:41] Number of IDs at 50%, 5%, 1%, 0.1% FDR: 302, 69, 0, 0
[459:41] Calibrating retention times
[459:41] 50 precursors used for iRT estimation.
[459:41] Processing batch #5 out of 4746 
[459:41] Precursor search
[459:55] Optimising weights
Ids at 10% FDR using TC scoring: 66
Ids at 10% FDR using TC selection: 66
Averages: 
0.0114532 -0.00762733 0 -0.0111578 0.0412745 0.0781069 
Weights: 
2.52962 1.82749 0 0 0.723378 0.613393 
[459:56] Calculating q-values
[459:56] Number of IDs at 50%, 5%, 1%, 0.1% FDR: 354, 76, 0, 0
[459:56] Calibrating retention times
[459:56] 50 precursors used for iRT estimation.
[459:56] Processing batches #6-7 out of 4746 
[459:56] Precursor search
[460:25] Optimising weights
Ids at 10% FDR using TC scoring: 87
Ids at 10% FDR using TC selection: 87
Averages: 
0.039416 0.0670952 0 0.0236083 -0.0595482 0.0492513 
Weights: 
5.05296 2.39614 0 0 1.59771 2.30334 
[460:25] Calculating q-values
[460:26] Number of IDs at 50%, 5%, 1%, 0.1% FDR: 395, 101, 0, 0
[460:26] Calibrating retention times
[460:26] 50 precursors used for iRT estimation.
[460:26] Precursor search
[460:27] Optimising weights
Ids at 10% FDR using TC scoring: 87
Ids at 10% FDR using TC selection: 87
Averages: 
0.039416 0.0670952 0 0.0236083 -0.0595482 0.0492513 
Weights: 
5.05296 2.39614 0 0 1.59771 2.30334 
[460:27] Calculating q-values
[460:27] Number of IDs at 50%, 5%, 1%, 0.1% FDR: 395, 101, 0, 0
[460:27] Calibrating retention times
[460:27] 50 precursors used for iRT estimation.
[460:27] Mass correction transform (101 precursors): -4.75335e-09 -0.00289121 4.81776e-06 
[460:27] M/z SD: 2.58963 ppm
[460:27] Top 70% mass accuracy: 3.13455 ppm
[460:27] Top 70% mass accuracy without correction: 4.40794ppm
[460:27] MS1 mass correction transform (97 precursors): -2.76198e-08 -0.0121469 3.70888e-05 
[460:28] Top 70% MS1 mass accuracy: 3.81443 ppm
[460:28] Top 70% MS1 mass accuracy without correction: 3.71194ppm
[460:28] No MS1 mass correction required
[460:28] Recalibrating with mass accuracy 1.56727e-05, 1.85597e-05 (MS2, MS1)
[460:28] Processing batch #1 out of 4746 
[460:28] Precursor search
[460:42] Optimising weights
Ids at 10% FDR using TC scoring: 27
Ids at 10% FDR using TC selection: 27
Averages: 
0.028687 0.0264129 0 0.0542982 0.0575501 -0.00518545 
Weights: 
2.40785 0.0491556 0 2.39673 0.478376 0.239331 
[460:42] Calculating q-values
[460:42] Number of IDs at 50%, 5%, 1%, 0.1% FDR: 105, 22, 0, 0
[460:42] Calculating q-values
[460:42] Number of IDs at 50%, 5%, 1%, 0.1% FDR: 67, 29, 0, 0
[460:42] Calibrating retention times
[460:42] 50 precursors used for iRT estimation.
[460:42] Processing batch #2 out of 4746 
[460:42] Precursor search
[460:57] Optimising weights
Ids at 10% FDR using TC scoring: 39
Ids at 10% FDR using TC selection: 39
Averages: 
0.0209893 0.0110028 0 0.0294945 0.0533885 0.0205418 
Weights: 
4.28648 0 0 0 0.637213 0.264779 
[460:57] Calculating q-values
[460:57] Number of IDs at 50%, 5%, 1%, 0.1% FDR: 178, 33, 0, 0
[460:57] Calculating q-values
[460:57] Number of IDs at 50%, 5%, 1%, 0.1% FDR: 132, 41, 0, 0
[460:57] Calibrating retention times
[460:57] 50 precursors used for iRT estimation.
[460:57] Processing batch #3 out of 4746 
[460:57] Precursor search
[461:11] Optimising weights
Ids at 10% FDR using TC scoring: 56
Ids at 10% FDR using TC selection: 56
Averages: 
0.0292504 0.0269557 0 0.0439746 0.0975662 -0.00172421 
Weights: 
4.68225 0 0 0 0.690067 0.449768 
[461:11] Calculating q-values
[461:11] Number of IDs at 50%, 5%, 1%, 0.1% FDR: 217, 50, 0, 0
[461:11] Calculating q-values
[461:11] Number of IDs at 50%, 5%, 1%, 0.1% FDR: 149, 59, 0, 0
[461:11] Calibrating retention times
[461:11] 50 precursors used for iRT estimation.
[461:11] Processing batch #4 out of 4746 
[461:11] Precursor search
[461:26] Optimising weights
Ids at 10% FDR using TC scoring: 57
Ids at 10% FDR using TC selection: 57
Averages: 
0.0223177 0.0231409 0 0.0325267 0.0861532 0.0450192 
Weights: 
4.49626 0 0 0 0.79573 0.4437 
[461:26] Calculating q-values
[461:26] Number of IDs at 50%, 5%, 1%, 0.1% FDR: 272, 59, 0, 0
[461:26] Calibrating retention times
[461:26] 50 precursors used for iRT estimation.
[461:26] Processing batch #5 out of 4746 
[461:26] Precursor search
[461:40] Optimising weights
Ids at 10% FDR using TC scoring: 76
Ids at 10% FDR using TC selection: 76
Averages: 
0.00678655 0.0125111 0 0.0274749 0.0998618 0.0335968 
Weights: 
4.41466 0 0 0.122609 0.911763 0.500692 
[461:40] Calculating q-values
[461:40] Number of IDs at 50%, 5%, 1%, 0.1% FDR: 335, 79, 0, 0
[461:40] Calibrating retention times
[461:41] 50 precursors used for iRT estimation.
[461:41] Processing batches #6-7 out of 4746 
[461:41] Precursor search
[462:09] Optimising weights
Ids at 10% FDR using TC scoring: 100
Ids at 10% FDR using TC selection: 100
Averages: 
0.00901306 0.00711245 0 0.0532466 0.12547 -0.0819737 
Weights: 
7.39327 0 0 0.731647 1.40152 0.890521 
[462:09] Calculating q-values
[462:09] Number of IDs at 50%, 5%, 1%, 0.1% FDR: 434, 115, 100, 0
[462:09] Calibrating retention times
[462:09] 100 precursors used for iRT estimation.
[462:09] Processing batches #8-9 out of 4746 
[462:09] Precursor search
[462:40] Optimising weights
Ids at 10% FDR using TC scoring: 112
Ids at 10% FDR using TC selection: 112
Averages: 
0.00448272 0.01582 0 0.0755015 0.275135 -0.15205 
Weights: 
6.96316 0 0 1.8024 1.98107 0.896176 
[462:40] Calculating q-values
[462:40] Number of IDs at 50%, 5%, 1%, 0.1% FDR: 484, 127, 121, 0
[462:40] Calibrating retention times
[462:40] 121 precursors used for iRT estimation.
[462:40] Processing batches #10-11 out of 4746 
[462:40] Precursor search
[463:09] Optimising weights
Ids at 10% FDR using TC scoring: 135
Ids at 10% FDR using TC selection: 135
Averages: 
0.0455 0.0639699 0 0.100412 0.167095 -0.0714895 
Weights: 
8.24828 0 0 0.485246 1.46851 0 
[463:09] Calculating q-values
[463:09] Number of IDs at 50%, 5%, 1%, 0.1% FDR: 529, 149, 111, 0
[463:09] Calibrating retention times
[463:09] 111 precursors used for iRT estimation.
[463:09] Processing batches #12-14 out of 4746 
[463:09] Precursor search
[463:54] Optimising weights
Ids at 10% FDR using TC scoring: 145
Ids at 10% FDR using TC selection: 146
Averages: 
0.0393279 0.0592545 0 0.0951031 0.21247 -0.0682044 
Weights: 
8.09972 0 0 0 1.46117 0.116964 
[463:54] Calculating q-values
[463:55] Number of IDs at 50%, 5%, 1%, 0.1% FDR: 666, 181, 137, 0
[463:55] Calibrating retention times
[463:55] 137 precursors used for iRT estimation.
[463:55] Processing batches #15-17 out of 4746 
[463:55] Precursor search
[464:38] Optimising weights
Ids at 10% FDR using TC scoring: 175
Ids at 10% FDR using TC selection: 175
Averages: 
0.0305042 0.0632089 0 0.0731187 0.275143 -0.128418 
Weights: 
6.85492 0 0 0.893681 1.88762 0.480204 
[464:38] Calculating q-values
[464:39] Number of IDs at 50%, 5%, 1%, 0.1% FDR: 773, 213, 149, 0
[464:39] Calibrating retention times
[464:39] 149 precursors used for iRT estimation.
[464:39] Processing batches #18-21 out of 4746 
[464:39] Precursor search
[465:37] Optimising weights
Ids at 10% FDR using TC scoring: 202
Ids at 10% FDR using TC selection: 204
Averages: 
0.0333563 0.0531681 0 0.0693981 0.30036 -0.0542401 
Weights: 
6.68392 0 0 0.44032 1.90129 0.636826 
[465:37] Calculating q-values
[465:37] Number of IDs at 50%, 5%, 1%, 0.1% FDR: 933, 252, 190, 0
[465:37] Calibrating retention times
[465:37] 190 precursors used for iRT estimation.
[465:37] Processing batches #22-26 out of 4746 
[465:37] Precursor search
[466:50] Optimising weights
Ids at 10% FDR using TC scoring: 243
Ids at 10% FDR using TC selection: 245
Averages: 
0.0312668 0.0542358 0 0.0766824 0.302788 -0.0289384 
Weights: 
6.77938 0.233125 0 0.800034 1.7244 0.442371 
[466:50] Calculating q-values
[466:50] Number of IDs at 50%, 5%, 1%, 0.1% FDR: 1150, 314, 239, 0
[466:50] Calibrating retention times
[466:50] 239 precursors used for iRT estimation.
[466:50] Processing batches #27-32 out of 4746 
[466:50] Precursor search
[468:17] Optimising weights
Ids at 10% FDR using TC scoring: 283
Ids at 10% FDR using TC selection: 286
Averages: 
0.0250103 0.0444676 0 0.0692683 0.239745 -0.00969193 
Weights: 
7.28732 0 0 0.675509 1.63957 0.400224 
[468:17] Calculating q-values
[468:17] Number of IDs at 50%, 5%, 1%, 0.1% FDR: 1350, 362, 307, 0
[468:17] Calibrating retention times
[468:17] 307 precursors used for iRT estimation.
[468:17] Processing batches #33-39 out of 4746 
[468:17] Precursor search
[469:57] Optimising weights
Ids at 10% FDR using TC scoring: 341
Ids at 10% FDR using TC selection: 344
Averages: 
0.023692 0.0549725 0 0.0733747 0.195156 -0.0196178 
Weights: 
7.36436 0 0 0.664587 1.56335 0.108947 
[469:57] Calculating q-values
[469:58] Number of IDs at 50%, 5%, 1%, 0.1% FDR: 1547, 445, 346, 0
[469:58] Calibrating retention times
[469:58] 346 precursors used for iRT estimation.
[469:58] Processing batches #40-47 out of 4746 
[469:58] Precursor search
[471:53] Optimising weights
Ids at 10% FDR using TC scoring: 422
Ids at 10% FDR using TC selection: 425
Averages: 
0.0150012 0.0432812 0 0.0578919 0.189606 -0.0379369 
Weights: 
6.98204 0 0 1.06603 1.58868 0.0847524 
[471:53] Calculating q-values
[471:53] Number of IDs at 50%, 5%, 1%, 0.1% FDR: 1981, 545, 406, 0
[471:53] Calibrating retention times
[471:53] 406 precursors used for iRT estimation.
[471:53] Processing batches #48-57 out of 4746 
[471:53] Precursor search
[474:18] Optimising weights
Ids at 10% FDR using TC scoring: 513
Ids at 10% FDR using TC selection: 517
Averages: 
0.0158824 0.0482533 0 0.0542758 0.202805 -0.0411632 
Weights: 
6.82677 0.0724902 0 0.951906 1.62522 0 
[474:18] Calculating q-values
[474:18] Number of IDs at 50%, 5%, 1%, 0.1% FDR: 2424, 659, 505, 0
[474:18] Calibrating retention times
[474:19] 505 precursors used for iRT estimation.
[474:19] Processing batches #58-69 out of 4746 
[474:19] Precursor search
[477:12] Optimising weights
Ids at 10% FDR using TC scoring: 632
Ids at 10% FDR using TC selection: 636
Averages: 
0.0179367 0.0428632 0 0.0590435 0.165858 -0.0363634 
Weights: 
6.82264 0 0 0.926555 1.63606 0.148073 
[477:12] Calculating q-values
[477:12] Number of IDs at 50%, 5%, 1%, 0.1% FDR: 2919, 809, 616, 0
[477:12] Calibrating retention times
[477:12] 616 precursors used for iRT estimation.
[477:12] Processing batches #70-83 out of 4746 
[477:12] Precursor search
[480:34] Optimising weights
Ids at 10% FDR using TC scoring: 759
Ids at 10% FDR using TC selection: 765
Averages: 
0.0156991 0.0313715 0 0.0529888 0.153785 -0.0134667 
Weights: 
7.25689 0 0 0.774692 1.64508 0.101796 
[480:34] Calculating q-values
[480:34] Number of IDs at 50%, 5%, 1%, 0.1% FDR: 3455, 977, 722, 0
[480:34] Calibrating retention times
[480:34] 722 precursors used for iRT estimation.
[480:34] Processing batches #84-100 out of 4746 
[480:34] Precursor search
[484:46] Optimising weights
Ids at 10% FDR using TC scoring: 918
Ids at 10% FDR using TC selection: 926
Averages: 
0.0208341 0.0244133 0 0.0529257 0.0998986 -0.0036643 
Weights: 
7.59889 0 0 0.0140577 1.51255 0.0670378 
[484:46] Calculating q-values
[484:47] Number of IDs at 50%, 5%, 1%, 0.1% FDR: 4228, 1192, 857, 0
[484:47] Calibrating retention times
[484:47] 857 precursors used for iRT estimation.
[484:47] Precursor search
[485:05] Optimising weights
Ids at 10% FDR using TC scoring: 925
Ids at 10% FDR using TC selection: 926
Averages: 
0.0216576 0.0232734 0 0.0508888 0.10245 0.00613262 
Weights: 
7.62191 0 0 0 1.51285 0.0242904 
[485:05] Calculating q-values
[485:06] Number of IDs at 50%, 5%, 1%, 0.1% FDR: 4226, 1196, 850, 0
[485:06] Precursor search
[485:06] Optimising weights
Ids at 10% FDR using TC scoring: 925
Ids at 10% FDR using TC selection: 926
Averages: 
0.0216576 0.0232734 0 0.0508888 0.10245 0.00613262 
Weights: 
7.62187 0 0 0 1.51282 0.0245356 
[485:06] Calculating q-values
[485:06] Number of IDs at 50%, 5%, 1%, 0.1% FDR: 4226, 1196, 850, 0
[485:06] Precursor search
[485:24] Optimising weights
Ids at 10% FDR using TC scoring: 925
Ids at 10% FDR using TC selection: 926
Averages: 
0.0216576 0.0232734 0 0.0508888 0.10245 0.00613262 
Weights: 
7.62187 0 0 0 1.51282 0.0245356 
[485:24] Calculating q-values
[485:24] Number of IDs at 50%, 5%, 1%, 0.1% FDR: 4226, 1196, 850, 0
[485:24] Calibrating retention times
[485:25] 850 precursors used for iRT estimation.
[485:25] RT window set to 7.93247
[485:25] Ion mobility window set to 0.0792919
[485:25] Peak width: 4.012
[485:25] Scan window radius set to 8
[485:25] Mass correction transform (1183 precursors): 1.90364e-09 0.00121326 -6.33956e-06 
[485:25] M/z SD: 2.61907 ppm
[485:25] Top 70% mass accuracy: 3.60702 ppm
[485:25] Top 70% mass accuracy without correction: 5.21441ppm
[485:25] MS1 mass correction transform (1026 precursors): -1.52021e-08 -0.00807683 2.21714e-05 
[485:25] Top 70% MS1 mass accuracy: 3.1497 ppm
[485:25] Top 70% MS1 mass accuracy without correction: 3.48068ppm
[485:25] Refining mass correction
[485:25] Calibrating retention times
[485:25] Mass correction transform (828 precursors): 2.35528e-09 0.00149256 -7.12501e-06 
[485:25] M/z SD: 2.62211 ppm
[485:25] Top 70% mass accuracy: 3.62122 ppm
[485:25] Top 70% mass accuracy without correction: 5.21441ppm
[485:25] MS1 mass correction transform (718 precursors): -1.85413e-08 -0.0100241 2.73868e-05 
[485:25] Top 70% MS1 mass accuracy: 3.10973 ppm
[485:25] Top 70% MS1 mass accuracy without correction: 3.48068ppm
[485:25] Recommended MS1 mass accuracy setting: 15.5487 ppm
[485:25] Processing batch #1 out of 4746 
[485:25] Precursor search
[485:28] Optimising weights
Ids at 10% FDR using TC scoring: 17
Ids at 10% FDR using TC selection: 17
Averages: 
0.0160958 0.00515545 0 0.0218525 0.00730383 0.036748 
Weights: 
3.41969 0 0 3.68907 0 0.367894 
[485:28] Calculating q-values
[485:28] Number of IDs at 50%, 5%, 1%, 0.1% FDR: 82, 26, 0, 0
[485:28] Calibrating retention times
[485:29] 50 precursors used for iRT estimation.
[485:29] Processing batch #2 out of 4746 
[485:29] Precursor search
[485:31] Optimising weights
Ids at 10% FDR using TC scoring: 28
Ids at 10% FDR using TC selection: 28
Averages: 
0.0291258 0.0211087 0 0.0257789 0.0365326 0.0203209 
Weights: 
4.52669 0 0 2.66815 0.0144444 0.2617 
[485:32] Calculating q-values
[485:32] Number of IDs at 50%, 5%, 1%, 0.1% FDR: 241, 46, 0, 0
[485:32] Calibrating retention times
[485:32] 50 precursors used for iRT estimation.
[485:32] Processing batch #3 out of 4746 
[485:32] Precursor search
[485:34] Optimising weights
Ids at 10% FDR using TC scoring: 46
Ids at 10% FDR using TC selection: 46
Averages: 
0.028091 0.0201003 0 0.023734 0.039611 0.0257449 
Weights: 
4.30772 0 0 1.94815 0.429982 0.154527 
[485:35] Calculating q-values
[485:35] Number of IDs at 50%, 5%, 1%, 0.1% FDR: 158, 49, 0, 0
[485:35] Calibrating retention times
[485:35] 50 precursors used for iRT estimation.
[485:35] Processing batch #4 out of 4746 
[485:35] Precursor search
[485:38] Optimising weights
Ids at 10% FDR using TC scoring: 58
Ids at 10% FDR using TC selection: 58
Averages: 
0.0288271 0.0202306 0 0.0234771 0.052096 0.0181343 
Weights: 
4.48692 0 0 2.05764 0.406647 0 
[485:38] Calculating q-values
[485:38] Number of IDs at 50%, 5%, 1%, 0.1% FDR: 208, 61, 0, 0
[485:38] Calibrating retention times
[485:38] 50 precursors used for iRT estimation.
[485:38] Processing batch #5 out of 4746 
[485:38] Precursor search
[485:41] Optimising weights
Ids at 10% FDR using TC scoring: 57
Ids at 10% FDR using TC selection: 57
Averages: 
0.0317498 0.0288413 0 0.0176853 0.0363394 0.0303869 
Weights: 
3.44096 0 0 1.63553 0.598393 0.258573 
[485:41] Calculating q-values
[485:41] Number of IDs at 50%, 5%, 1%, 0.1% FDR: 184, 70, 0, 0
[485:41] Calibrating retention times
[485:41] 50 precursors used for iRT estimation.
[485:41] Processing batches #6-7 out of 4746 
[485:41] Precursor search
[485:47] Optimising weights
Ids at 10% FDR using TC scoring: 112
Ids at 10% FDR using TC selection: 113
Averages: 
0.0389143 0.0280763 0 0.0222136 0.0605054 0.00312233 
Weights: 
3.31685 0 0 1.90624 0.651765 0.388403 
[485:47] Calculating q-values
[485:47] Number of IDs at 50%, 5%, 1%, 0.1% FDR: 298, 99, 0, 0
[485:47] Calculating q-values
[485:47] Number of IDs at 50%, 5%, 1%, 0.1% FDR: 271, 115, 0, 0
[485:47] Calibrating retention times
[485:47] 50 precursors used for iRT estimation.
[485:47] Processing batches #8-9 out of 4746 
[485:47] Precursor search
[485:52] Optimising weights
Ids at 10% FDR using TC scoring: 141
Ids at 10% FDR using TC selection: 142
Averages: 
0.0354124 0.037636 0 0.0493573 0.00227625 -0.05812 
Weights: 
5.56021 0 0 2.25296 1.35097 0.561009 
[485:52] Calculating q-values
[485:52] Number of IDs at 50%, 5%, 1%, 0.1% FDR: 415, 136, 114, 0
[485:52] Calculating q-values
[485:52] Number of IDs at 50%, 5%, 1%, 0.1% FDR: 352, 147, 120, 0
[485:52] Calibrating retention times
[485:52] 120 precursors used for iRT estimation.
[485:52] Processing batches #10-11 out of 4746 
[485:52] Precursor search
[485:57] Optimising weights
Ids at 10% FDR using TC scoring: 154
Ids at 10% FDR using TC selection: 154
Averages: 
0.090386 0.0953481 0 0.11426 0.129291 -0.0125102 
Weights: 
5.60898 0 0 2.17848 1.35771 0.83323 
[485:58] Calculating q-values
[485:58] Number of IDs at 50%, 5%, 1%, 0.1% FDR: 448, 147, 0, 0
[485:58] Calculating q-values
[485:58] Number of IDs at 50%, 5%, 1%, 0.1% FDR: 374, 155, 113, 0
[485:58] Calibrating retention times
[485:58] 113 precursors used for iRT estimation.
[485:58] Processing batches #12-14 out of 4746 
[485:58] Precursor search
[486:05] Optimising weights
Ids at 10% FDR using TC scoring: 164
Ids at 10% FDR using TC selection: 164
Averages: 
0.0805767 0.0774146 0 0.0987353 0.107873 0.017168 
Weights: 
5.77192 0 0 1.37869 1.50215 0.573245 
[486:05] Calculating q-values
[486:05] Number of IDs at 50%, 5%, 1%, 0.1% FDR: 527, 164, 111, 0
[486:05] Calibrating retention times
[486:06] 111 precursors used for iRT estimation.
[486:06] Processing batches #15-17 out of 4746 
[486:06] Precursor search
[486:13] Optimising weights
Ids at 10% FDR using TC scoring: 197
Ids at 10% FDR using TC selection: 199
Averages: 
0.10381 0.099268 0 0.128724 0.186313 -0.0735912 
Weights: 
5.93232 0 0 0.797376 1.67244 0.663436 
[486:13] Calculating q-values
[486:13] Number of IDs at 50%, 5%, 1%, 0.1% FDR: 605, 189, 127, 0
[486:13] Calculating q-values
[486:13] Number of IDs at 50%, 5%, 1%, 0.1% FDR: 561, 211, 128, 0
[486:13] Calibrating retention times
[486:13] 128 precursors used for iRT estimation.
[486:13] Processing batches #18-21 out of 4746 
[486:13] Precursor search
[486:23] Optimising weights
Ids at 10% FDR using TC scoring: 241
Ids at 10% FDR using TC selection: 244
Averages: 
0.102375 0.112919 0 0.133179 0.195958 -0.0496137 
Weights: 
5.63236 0 0 1.04537 1.70742 0.512089 
[486:23] Calculating q-values
[486:23] Number of IDs at 50%, 5%, 1%, 0.1% FDR: 832, 233, 162, 0
[486:23] Calculating q-values
[486:23] Number of IDs at 50%, 5%, 1%, 0.1% FDR: 704, 263, 159, 0
[486:23] Calibrating retention times
[486:23] 159 precursors used for iRT estimation.
[486:23] Processing batches #22-26 out of 4746 
[486:23] Precursor search
WARNING: DIA-NN might run out of memory; if this happens, consider adjusting the 'Speed and RAM usage' option
[486:35] Optimising weights
Ids at 10% FDR using TC scoring: 277
Ids at 10% FDR using TC selection: 277
Averages: 
0.105523 0.104836 0 0.119524 0.206977 0.0275806 
Weights: 
5.00612 0 0 0.882008 1.91855 0.529262 
[486:35] Calculating q-values
[486:35] Number of IDs at 50%, 5%, 1%, 0.1% FDR: 952, 268, 183, 0
[486:35] Calculating q-values
[486:35] Number of IDs at 50%, 5%, 1%, 0.1% FDR: 864, 292, 176, 0
[486:35] Calibrating retention times
[486:35] 176 precursors used for iRT estimation.
[486:35] Processing batches #27-32 out of 4746 
[486:35] Precursor search
[486:50] Optimising weights
Ids at 10% FDR using TC scoring: 323
Ids at 10% FDR using TC selection: 325
Averages: 
0.0838432 0.0765757 0 0.0838969 0.145555 -0.00500163 
Weights: 
6.16779 0 0 0 1.69583 0.404274 
[486:50] Calculating q-values
[486:50] Number of IDs at 50%, 5%, 1%, 0.1% FDR: 1145, 322, 224, 0
[486:50] Calculating q-values
[486:50] Number of IDs at 50%, 5%, 1%, 0.1% FDR: 977, 329, 202, 0
[486:50] Calibrating retention times
[486:50] 202 precursors used for iRT estimation.
[486:50] Processing batches #33-39 out of 4746 
[486:50] Precursor search
[487:07] Optimising weights
Ids at 10% FDR using TC scoring: 365
Ids at 10% FDR using TC selection: 368
Averages: 
0.0844405 0.081116 0 0.0919018 0.195166 0.0191666 
Weights: 
5.79888 0 0 0 1.84412 0.476343 
[487:07] Calculating q-values
[487:07] Number of IDs at 50%, 5%, 1%, 0.1% FDR: 1397, 366, 248, 0
[487:07] Calibrating retention times
[487:07] 248 precursors used for iRT estimation.
[487:07] Processing batches #40-47 out of 4746 
[487:07] Precursor search
[487:26] Optimising weights
Ids at 10% FDR using TC scoring: 474
Ids at 10% FDR using TC selection: 477
Averages: 
0.0828624 0.0847652 0 0.0950665 0.195015 -0.0427715 
Weights: 
6.26326 0 0 0 1.73253 0.405071 
[487:26] Calculating q-values
[487:27] Number of IDs at 50%, 5%, 1%, 0.1% FDR: 1734, 471, 318, 0
[487:27] Calculating q-values
[487:27] Number of IDs at 50%, 5%, 1%, 0.1% FDR: 1499, 489, 287, 0
[487:27] Calibrating retention times
[487:27] 287 precursors used for iRT estimation.
[487:27] Processing batches #48-57 out of 4746 
[487:27] Precursor search
[487:51] Optimising weights
Ids at 10% FDR using TC scoring: 594
Ids at 10% FDR using TC selection: 596
Averages: 
0.0839016 0.0730262 0 0.091226 0.200633 -0.0137308 
Weights: 
6.45387 0 0 0 1.66342 0.226467 
[487:51] Calculating q-values
[487:52] Number of IDs at 50%, 5%, 1%, 0.1% FDR: 2040, 588, 403, 0
[487:52] Calculating q-values
[487:52] Number of IDs at 50%, 5%, 1%, 0.1% FDR: 1863, 604, 357, 0
[487:52] Calibrating retention times
[487:52] 357 precursors used for iRT estimation.
[487:52] Processing batches #58-69 out of 4746 
[487:52] Precursor search
[488:21] Optimising weights
Ids at 10% FDR using TC scoring: 731
Ids at 10% FDR using TC selection: 733
Averages: 
0.0738589 0.0641022 0 0.0823935 0.172658 -0.00335711 
Weights: 
6.69887 0 0 0 1.54669 0.150504 
[488:21] Calculating q-values
[488:21] Number of IDs at 50%, 5%, 1%, 0.1% FDR: 2580, 733, 464, 0
[488:21] Calibrating retention times
[488:22] 464 precursors used for iRT estimation.
[488:22] Processing batches #70-83 out of 4746 
[488:22] Precursor search
[488:56] Optimising weights
Ids at 10% FDR using TC scoring: 871
Ids at 10% FDR using TC selection: 877
Averages: 
0.0774063 0.0614961 0 0.0887996 0.171767 0.0186331 
Weights: 
6.46852 0 0 0.153444 1.56406 0.234694 
[488:56] Calculating q-values
[488:56] Number of IDs at 50%, 5%, 1%, 0.1% FDR: 3068, 855, 590, 0
[488:56] Calculating q-values
[488:56] Number of IDs at 50%, 5%, 1%, 0.1% FDR: 2771, 882, 563, 0
[488:56] Calibrating retention times
[488:56] 563 precursors used for iRT estimation.
[488:56] Processing batches #84-100 out of 4746 
[488:56] Precursor search
[489:37] Optimising weights
Ids at 10% FDR using TC scoring: 1067
Ids at 10% FDR using TC selection: 1070
Averages: 
0.0664321 0.0564265 0 0.0759211 0.165307 0.0215672 
Weights: 
5.61994 0 0 0.992868 1.51966 0.325525 
[489:37] Calculating q-values
[489:37] Number of IDs at 50%, 5%, 1%, 0.1% FDR: 3707, 1052, 735, 0
[489:37] Calculating q-values
[489:37] Number of IDs at 50%, 5%, 1%, 0.1% FDR: 3428, 1082, 639, 0
[489:37] Calibrating retention times
[489:37] 639 precursors used for iRT estimation.
[489:37] Precursor search
[489:38] Optimising weights
Ids at 10% FDR using TC scoring: 1068
Ids at 10% FDR using TC selection: 1071
Averages: 
0.0660245 0.0520395 0 0.071791 0.168569 0.0373844 0 0 0 0 |***| 0 0 0 0 0 0 0 0 0.0597938 0.062316 |***| 0.655996 0.0934199 0.764265 0.117666 0.0772791 0.38354 0.0268837 0 0.0444219 0.0273217 |***| 0 0.28225 0.186707 0.218509 0.345631 0.348099 -0.0453007 -0.00650127 0 
Weights: 
1e-09 0 0 0.138059 0.715646 0 0 0 0 0 |***| 0 0 0 0 0 0 0 0 0 3.54766 |***| 0 0.137564 0 0.334178 0 0.21962 0.217604 0 0.0645182 0.360795 |***| 0 0.393519 0.276474 0.00862218 0.423727 0.113512 -2.2184 -0.879342 0 
[489:38] Calculating q-values
[489:38] Number of IDs at 50%, 5%, 1%, 0.1% FDR: 4879, 1445, 863, 0
[489:38] Trying mass accuracy 4.34546 ppm
[489:38] Precursor search
[493:46] Optimising weights
Ids at 10% FDR using TC scoring: 1149
Ids at 10% FDR using TC selection: 1149
Averages: 
0.0650081 0.0530186 0 0.0724862 0.152767 0.0394171 0 0 0 0 |***| 0 0 0 0 0 0 0 0 0.0584386 0.0537427 |***| 0.688573 0.0860149 0.72326 0.0941351 0.0574952 0.301389 0.0566532 0 0.0672404 0.0375763 |***| 0 0.203085 0.139279 0.295866 0.333342 0.291231 -0.0356668 -0.0056903 0 
Weights: 
1e-09 0 0 0 1.04655 0 0 0 0 0 |***| 0 0 0 0 0 0 0 0 0 3.16065 |***| 0 0.153072 0 0 0.106588 0.137642 0.182747 0 0.716888 0.0209477 |***| 0 0.324403 0.290121 0.121455 0.362507 0.0682597 -2.06733 -0.655371 0 
[493:47] Calculating q-values
[493:47] Number of IDs at 50%, 5%, 1%, 0.1% FDR: 5236, 1508, 923, 0
[493:47] Trying mass accuracy 5.21456 ppm
[493:47] Precursor search
[497:59] Optimising weights
Ids at 10% FDR using TC scoring: 1298
Ids at 10% FDR using TC selection: 1298
Averages: 
0.0734104 0.058038 0 0.0684907 0.141893 0.0628661 0 0 0 0 |***| 0 0 0 0 0 0 0 0 0.0629327 0.0575062 |***| 0.718724 0.0822167 0.628534 0.0993017 0.0893487 0.315833 0.0589411 0 0.0811944 0.058582 |***| 0 0.137858 0.121897 0.257784 0.328247 0.279832 -0.0137013 -0.00791301 0 
Weights: 
1e-09 0 0 0 0.927052 0 0 0 0 0 |***| 0 0 0 0 0 0 0 0 0.870731 2.32527 |***| 0 0.354895 0 0 0.462246 0 0.562865 0 0.771121 0.29302 |***| 0 0.266384 0.459106 0.0553878 0.26759 0.090091 -1.91338 -0.784266 0 
[497:59] Calculating q-values
[498:00] Number of IDs at 50%, 5%, 1%, 0.1% FDR: 5543, 1715, 1130, 0
[498:00] Trying mass accuracy 6.25747 ppm
[498:00] Precursor search
[502:21] Optimising weights
Ids at 10% FDR using TC scoring: 1382
Ids at 10% FDR using TC selection: 1382
Averages: 
0.0613109 0.0656941 0 0.0704433 0.157785 -0.0132526 0 0 0 0 |***| 0 0 0 0 0 0 0 0 0.0635197 0.0538268 |***| 0.631286 0.0827127 0.613747 0.10691 0.0661588 0.264647 0.0281724 0 0.0771167 0.0388761 |***| 0 0.117176 0.162998 0.104024 0.278673 0.199563 -0.00975336 -0.00528226 0 
Weights: 
1e-09 0 0 0.0830847 0.819749 0 0 0 0 0 |***| 0 0 0 0 0 0 0 0 1.53111 2.35402 |***| 0 0.0845737 0 0 0.739428 0 0.513698 0 0.467491 0.20737 |***| 0 0.144799 0.360515 0 0.264835 0.0783218 -1.20361 -0.534335 0 
[502:21] Calculating q-values
[502:22] Number of IDs at 50%, 5%, 1%, 0.1% FDR: 5918, 1829, 1309, 0
[502:22] Trying mass accuracy 7.50896 ppm
[502:22] Precursor search
[506:53] Optimising weights
Ids at 10% FDR using TC scoring: 1389
Ids at 10% FDR using TC selection: 1389
Averages: 
0.0661966 0.0746713 0 0.0628479 0.152167 -0.0101098 0 0 0 0 |***| 0 0 0 0 0 0 0 0 0.0678816 0.0575327 |***| 0.667304 0.0767118 0.496068 0.0997535 0.074423 0.277432 0.0260494 0 0.0826701 0.05291 |***| 0 0.158864 0.179155 0.125682 0.256195 0.172554 -0.0226034 -0.0108182 0 
Weights: 
1e-09 0 0 0 0.952494 0 0 0 0 0 |***| 0 0 0 0 0 0 0 0 2.00725 2.46635 |***| 0 0 0 0 0.628658 0.00192581 0.591735 0 0.279339 0 |***| 0 0.275287 0.282171 0 0.252971 0.0500167 -1.09801 -0.578271 0 
[506:53] Calculating q-values
[506:53] Number of IDs at 50%, 5%, 1%, 0.1% FDR: 6196, 1947, 1372, 0
[506:53] Trying mass accuracy 9.01075 ppm
[506:53] Precursor search
[511:27] Optimising weights
Ids at 10% FDR using TC scoring: 1497
Ids at 10% FDR using TC selection: 1497
Averages: 
0.0566694 0.0588889 0 0.0599617 0.137413 0.0136585 0 0 0 0 |***| 0 0 0 0 0 0 0 0 0.054393 0.0420421 |***| 0.60606 0.0684221 0.435374 0.09281 0.0650202 0.275312 0.0317846 0 0.0763935 0.0437771 |***| 0 0.151081 0.154155 0.145086 0.246241 0.144798 -0.00709623 -0.00920602 0 
Weights: 
1e-09 0 0 0.21123 0.854958 0 0 0 0 0 |***| 0 0 0 0 0 0 0 0 1.72904 2.48495 |***| 0 0 0 0 0.518969 0 0.500679 0 0.603374 0.165786 |***| 0 0.33156 0.235847 0 0.263124 0.0811105 -0.894427 -0.452224 0 
[511:28] Calculating q-values
[511:28] Number of IDs at 50%, 5%, 1%, 0.1% FDR: 6351, 1984, 1344, 0
[511:28] Trying mass accuracy 10.8129 ppm
[511:28] Precursor search
[516:17] Optimising weights
Ids at 10% FDR using TC scoring: 1398
Ids at 10% FDR using TC selection: 1398
Averages: 
0.0645305 0.0546167 0 0.0615259 0.148599 0.0355737 0 0 0 0 |***| 0 0 0 0 0 0 0 0 0.0586015 0.0431243 |***| 0.59953 0.0674328 0.341907 0.0928429 0.0530187 0.212251 0.0431198 0 0.0826945 0.0595483 |***| 0 0.161683 0.14618 0.14473 0.304247 0.192911 -0.007551 -0.00786989 0 
Weights: 
1e-09 0 0 0 0.940736 0 0 0 0 0 |***| 0 0 0 0 0 0 0 0 1.36503 2.55887 |***| 0 0 0 0 0.587518 0 0.578857 0 0.782523 0.161472 |***| 0 0.335237 0.210315 0 0.23859 0.0416549 -1.34663 -0.162032 0 
[516:17] Calculating q-values
[516:18] Number of IDs at 50%, 5%, 1%, 0.1% FDR: 6060, 1956, 1367, 0
[516:18] Trying mass accuracy 12.9755 ppm
[516:18] Precursor search
[521:05] Optimising weights
Ids at 10% FDR using TC scoring: 1369
Ids at 10% FDR using TC selection: 1369
Averages: 
0.0572727 0.0554133 0 0.0502625 0.123626 0.045484 0 0 0 0 |***| 0 0 0 0 0 0 0 0 0.0496921 0.0440139 |***| 0.601089 0.0618824 0.18169 0.0711771 0.0726205 0.244056 0.0477404 0 0.0839089 0.0766989 |***| 0 0.134917 0.16229 0.11881 0.277646 0.197052 -0.0126378 -0.000652286 0 
Weights: 
1e-09 0 0 0 0.922736 0 0 0 0 0 |***| 0 0 0 0 0 0 0 0 1.01882 2.1285 |***| 0 0 0 0 0.611102 0 0.561746 0 0.626452 0.596806 |***| 0 0.0672342 0.234422 0 0.285343 0.0992898 -0.90331 -0.0103064 0 
[521:05] Calculating q-values
[521:05] Number of IDs at 50%, 5%, 1%, 0.1% FDR: 6167, 1933, 1394, 0
[521:05] Trying mass accuracy 15.5706 ppm
[521:06] Precursor search
[525:55] Optimising weights
Ids at 10% FDR using TC scoring: 1321
Ids at 10% FDR using TC selection: 1321
Averages: 
0.0622074 0.0629016 0 0.0551725 0.119606 0.0440827 0 0 0 0 |***| 0 0 0 0 0 0 0 0 0.0548603 0.0442214 |***| 0.6705 0.0717513 0.357315 0.0641751 0.0452908 0.227553 0.0432793 0 0.1066 0.107146 |***| 0 0.127523 0.166755 0.173623 0.300315 0.252077 -0.0239653 1.71316e-05 0 
Weights: 
1e-09 0 0 0 0.762358 0.0574723 0 0 0 0 |***| 0 0 0 0 0 0 0 0 1.02552 1.77108 |***| 0 0 0 0 0.576432 0 0.207498 0 1.22507 0.643338 |***| 0 0.283481 0.0818842 0.117872 0.0944589 0.191745 -0.969963 -0.0328124 0 
[525:56] Calculating q-values
[525:56] Number of IDs at 50%, 5%, 1%, 0.1% FDR: 6080, 1930, 1359, 0
[525:56] Optimised mass accuracy: 9.01075 ppm
[525:56] Precursor search
[531:19] Optimising weights
Ids at 10% FDR using TC scoring: 1551
Ids at 10% FDR using TC selection: 1551
Averages: 
0.0802306 0.0833353 0 0.0597386 0.149104 0.0266439 0 0 0 0 |***| 0 0 0 0 0 0 0 0 0.0757899 0.069681 |***| 0.560375 0.0704841 0.467576 0.100496 0.045863 0.221208 0.0372604 0.0024846 0.0743768 0.0371811 |***| 0.0681309 0.0908189 0.0833436 0.159419 0.249378 0.158191 -0.0214922 -0.00787021 -0.0181247 
Weights: 
1e-09 0 0 0 0.906889 0 0 0 0 0 |***| 0 0 0 0 0 0 0 0 0.917353 1.05148 |***| 0 0 0 0 0.113506 0.081774 0.151606 0.0762136 0.606893 0 |***| 0 0.200834 0.27911 0 0.24654 0.0130239 -1.30634 -0.505092 -1.80223 
[531:19] Calculating q-values
[531:20] Number of IDs at 50%, 5%, 1%, 0.1% FDR: 6112, 2073, 1630, 1180
[531:20] Calibrating retention times
[531:20] 1630 precursors used for iRT estimation.
[531:20] Precursor search
[531:20] Optimising weights
Ids at 10% FDR using TC scoring: 1574
Ids at 10% FDR using TC selection: 1548
Averages: 
0.0876211 0.0855237 0 0.0576681 0.155003 0.031537 0 0 0 0 |***| 0 0 0 0 0 0 0 0 0.0823649 0.0754738 |***| 0.577374 0.0719564 0.487816 0.101165 0.0414545 0.227512 0.0271682 0.0193793 0.0793303 0.0403246 |***| 0.0651678 0.101075 0.118587 0.111835 0.234004 0.17845 -0.0151753 -0.00695299 -0.0183779 
Weights: 
1e-09 0 0 0 0.992262 0 0 0 0 0 |***| 0 0 0 0 0 0 0 0 0.915189 0.924146 |***| 0 0 0 0 0.408436 0.0418213 0 0.17692 0.845279 0 |***| 0 0.315138 0.312005 0 0.247446 0 -1.51413 -0.584067 -2.47852 
[531:20] Calculating q-values
[531:21] Number of IDs at 50%, 5%, 1%, 0.1% FDR: 6635, 2064, 1534, 1106
[531:21] Reverting weights
[531:21] Preventing the linear classifier from using the full set of weights in the future
[531:21] Precursor search
[531:21] Optimising weights
Ids at 10% FDR using TC scoring: 1544
Ids at 10% FDR using TC selection: 1548
Averages: 
0.0806528 0.0835515 0 0.0586157 0.153657 0.0293519 0 0 0 0 |***| 0 0 0 0 0 0 0 0 0.0757609 0.0708159 |***| 0.557014 0.0702082 0.455715 0.103235 0.0460538 0.221115 0.0362313 0 0.0742179 0.0388861 |***| 0 0.0968036 0.0862268 0.171698 0.259165 0.169354 -0.0200328 -0.00868256 
Weights: 
1e-09 0 0 0 0.959877 0 0 0 0 0 |***| 0 0 0 0 0 0 0 0 1.0249 0.989517 |***| 0 0 0 0 0.193369 0.0719318 0.217244 0 0.612465 0 |***| 0 0.214865 0.278418 0 0.255448 0.029265 -1.50697 -0.571246 
[531:21] Calculating q-values
[531:21] Number of IDs at 50%, 5%, 1%, 0.1% FDR: 6214, 1989, 1452, 1020
[531:21] Reverting weights
[531:21] Precursor search
[531:22] Optimising weights
Ids at 10% FDR using TC scoring: 1544
Ids at 10% FDR using TC selection: 1548
Averages: 
0.0806528 0.0835515 0 0.0586157 0.153657 0.0293519 0 0 0 0 |***| 0 0 0 0 0 0 0 0 0.0757609 0.0708159 |***| 0.557014 0.0702082 0.455715 0.103235 0.0460538 0.221115 0.0362313 0 0.0742179 0.0388861 |***| 0 0.0968036 0.0862268 0.171698 0.259165 0.169354 -0.0200328 -0.00868256 
Weights: 
1e-09 0 0 0 0.959877 0 0 0 0 0 |***| 0 0 0 0 0 0 0 0 1.0249 0.989517 |***| 0 0 0 0 0.193369 0.0719318 0.217244 0 0.612465 0 |***| 0 0.214865 0.278418 0 0.255448 0.029265 -1.50697 -0.571246 
[531:22] Calculating q-values
[531:22] Number of IDs at 50%, 5%, 1%, 0.1% FDR: 6214, 1989, 1452, 1020
[531:22] Reverting weights
[531:22] Precursor search
[531:23] Optimising weights
Ids at 10% FDR using TC scoring: 1544
Ids at 10% FDR using TC selection: 1548
Averages: 
0.0806528 0.0835515 0 0.0586157 0.153657 0.0293519 0 0 0 0 |***| 0 0 0 0 0 0 0 0 0.0757609 0.0708159 |***| 0.557014 0.0702082 0.455715 0.103235 0.0460538 0.221115 0.0362313 0 0.0742179 0.0388861 |***| 0 0.0968036 0.0862268 0.171698 0.259165 0.169354 -0.0200328 -0.00868256 
Weights: 
1e-09 0 0 0 0.959877 0 0 0 0 0 |***| 0 0 0 0 0 0 0 0 1.0249 0.989517 |***| 0 0 0 0 0.193369 0.0719318 0.217244 0 0.612465 0 |***| 0 0.214865 0.278418 0 0.255448 0.029265 -1.50697 -0.571246 
[531:23] Calculating q-values
[531:23] Number of IDs at 50%, 5%, 1%, 0.1% FDR: 6214, 1989, 1452, 1020
[531:23] Reverting weights
[531:23] Restoring classifier and weights to
1 0 0 0 0 0 0 0 0 0 |***| 0 0 0 0 0 0 0 0 0 0 |***| 0 0 0 0 0 0 0 0 0 0 |***| 0 0 0 0 0 0 0 0 0 
[531:23] Precursor search

DIA-NN exited
DIA-NN-plotter.exe "C:\DIA-NN\1.8\report.stats.tsv" "C:\DIA-NN\1.8\report.tsv" "C:\DIA-NN\1.8\report.pdf"
PDF report will be generated in the background
vdemichev / DiaNN

DIA-NN exits during precursor search #153
