gnps_motif_27.m2m (GNPS library derived Mass2Motifs)

  • Motif name: gnps_motif_27.m2m
  • Motif set name: GNPS library derived Mass2Motifs
  • Annotation: (methylphenyl)sulfonylamino substructure [ClassyFire - Relevant terms - Substituents: Tosyl compound P-toluenesulfonamide Benzenesulfonamide Aminosulfonyl compound - Taxa: None]
  • Short annotation: (methylphenyl)sulfonylamino substructure
  • Comment: Derived from gnps_binned_005 ms2lda experiment id=191
  • Original motif: motif_27 (gnps_binned_005)
  • Resolver: mzspec:MOTIFDB::accession:151022
Feature Probability
fragment_155.0175 0.204577638612009
fragment_112.0775 0.131150300734141
fragment_474.1725 0.0731867058447746
fragment_258.0625 0.0404483676624993
fragment_144.1025 0.0364527345311441
fragment_112.1125 0.0322759750027652
fragment_226.0875 0.026212480119795
fragment_240.1025 0.0198681185349318
fragment_255.1725 0.018895241490705
fragment_475.1775 0.0188220656840589
fragment_263.1425 0.0149919142526645
fragment_156.0225 0.0124598110406126
fragment_679.4625 0.0121988039195302
fragment_563.3775 0.0121988039195302
loss_36.0825 0.0121988039195302
fragment_962.4425 0.0121987934918851
fragment_184.0425 0.0121802731426532
fragment_269.0275 0.0107666158695481
loss_48.0025 0.0102715500793379
fragment_277.1575 0.0101982782011854
fragment_113.0775 0.0101279154684741
loss_48.0075 0.00918600063173501
fragment_438.1725 0.00918600063173501
fragment_201.1275 0.00888106102770716
fragment_963.4475 0.00761250184793981
loss_28.0975 0.00745394368085681
fragment_226.0925 0.00738072242112017
fragment_500.1825 0.00734407044104173
loss_27.1375 0.00725878233427899
loss_27.0075 0.00692944756192891
fragment_210.0575 0.00668541614156244
fragment_157.0125 0.00646453412330748
fragment_293.1525 0.00636078214719122
loss_28.0075 0.00629517318555098
fragment_256.1725 0.00625846262802468
fragment_530.1925 0.00588039254400151
fragment_848.3925 0.00561210847252859
fragment_680.4525 0.00551452779923968
fragment_476.1775 0.00544119571761346
fragment_245.1125 0.00527057611601739
fragment_165.1025 0.00501435563709837
fragment_514.2025 0.00439235005641718
fragment_964.4425 0.00429475895741866
fragment_501.1925 0.004172698571491
fragment_564.3775 0.00401422494742265
fragment_259.0675 0.00389224910581151
fragment_139.0225 0.00378838540424222
loss_17.2025 0.00369708775923368
fragment_257.1875 0.00364826167395454
fragment_229.1525 0.00347648976746232
loss_26.1825 0.00341654332352805
fragment_241.1075 0.00336724253728785
fragment_345.1625 0.00335546043965557
fragment_282.1475 0.00331896265023914
loss_46.9975 0.00309934336726151
fragment_531.1975 0.00309934336726151
loss_18.1325 0.00306281338285574
fragment_145.1025 0.0030617810873825
fragment_258.1425 0.00300158913864512
fragment_439.1725 0.00275787377882789
fragment_227.0925 0.00263582063676033
fragment_199.1125 0.00262370035305565
fragment_264.1375 0.0025747803557742
fragment_231.1475 0.002497996989026
fragment_291.1375 0.00245287137691994
fragment_261.1275 0.00236743607655757
loss_47.0025 0.00231876074902779
fragment_375.1775 0.00214793181392787
fragment_278.1675 0.00209530124472608
loss_26.0025 0.00208700664996662
fragment_852.3925 0.00201382114499993
fragment_358.1775 0.00197722839251659
fragment_834.3575 0.00187964771922767
fragment_247.1425 0.00186738738371982
fragment_849.3925 0.00185525255090545
fragment_294.1575 0.00185518979983544
loss_35.2025 0.001806462214261
fragment_238.1425 0.00180633454907503
fragment_359.1825 0.00174547429345543
fragment_270.0325 0.00174542006165824
fragment_210.0625 0.00173327670929431
fragment_228.0825 0.00173313454620577
fragment_242.0975 0.00172134295785541
fragment_275.1375 0.00172107912513319
fragment_246.1125 0.00170888154097209
fragment_816.3525 0.00168448637264985
fragment_281.1525 0.00167214779284388
fragment_805.3775 0.0016356960360054
loss_42.0125 0.0016356960360054
fragment_85.0875 0.00157470811519983
fragment_422.1475 0.00155031294687761
loss_88.1125 0.00155031294687761
fragment_367.1325 0.00155026204458187
fragment_197.0575 0.00154613254544836

Please note: the following plots both show relative intensity such that the biggest feature (fragment / loss) has a value of 100%. For the absolute values, please see the table above.

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