gnps_motif_8.m2m (GNPS library derived Mass2Motifs)

  • Motif name: gnps_motif_8.m2m
  • Motif set name: GNPS library derived Mass2Motifs
  • Annotation: 2-(5-Methoxy-1H-indol-3-yl)ethyl substructure
  • Short annotation: 2-(5-Methoxy-1H-indol-3-yl)ethyl substructure
  • Comment: Derived from gnps_binned_005 ms2lda experiment id=191
  • Original motif: motif_8 (gnps_binned_005)
  • Resolver: mzspec:MOTIFDB::accession:151047
Feature Probability
fragment_174.0925 0.221303845219465
fragment_159.0675 0.118848304653144
loss_94.0525 0.0436862409171006
fragment_354.0525 0.0363494129327445
fragment_131.0725 0.0356149379560098
fragment_253.1225 0.028413365668299
fragment_117.0475 0.0233902479961404
loss_136.0725 0.022230211707162
fragment_175.0925 0.0211309654778066
fragment_143.0725 0.0183729572526569
fragment_259.9975 0.0175178579550971
loss_72.0425 0.0134249237426611
loss_158.0675 0.0127965580706662
fragment_133.0775 0.0121652627716328
loss_99.0725 0.0121169324893444
fragment_435.1575 0.0121168929636688
fragment_114.0375 0.0121166327340572
loss_55.0075 0.0120860199688787
loss_60.0825 0.0104447727567139
loss_161.0825 0.0100275739325938
fragment_636.2625 0.0099594071839986
loss_44.0475 0.00902754649090848
loss_29.0275 0.0087476182086419
fragment_117.0425 0.00842187824724199
fragment_160.0425 0.00811842370936787
fragment_188.0825 0.00806719100479691
loss_72.0725 0.00793723556289487
fragment_294.1075 0.00781550635605782
loss_27.9975 0.00765795011776588
fragment_160.0825 0.00726699395943196
loss_73.0525 0.00688270448808097
loss_60.0425 0.00685440334880501
loss_130.0725 0.00642224461438111
fragment_407.1575 0.00641022648860903
fragment_506.1875 0.00622848548504214
fragment_117.0575 0.00612704881667974
fragment_240.1225 0.00584075585063238
fragment_148.0375 0.0057638272099223
loss_125.1225 0.00568070364572795
loss_88.0775 0.00561740588396629
fragment_202.0975 0.00549536486565365
fragment_160.0725 0.00548684040942837
fragment_401.2075 0.0049444270434735
fragment_132.0675 0.00461680253056273
loss_76.0775 0.00458016884608153
fragment_104.0475 0.00457068183172827
loss_71.0225 0.00437507511486309
fragment_202.1175 0.00420595134968578
fragment_173.0825 0.00411927804791056
loss_56.0175 0.00403496489576121
loss_84.0325 0.0038076243866278
fragment_637.2725 0.00367153977747908
loss_173.0825 0.00364312134907291
fragment_277.0825 0.00363562561786461
fragment_106.0675 0.00360747870598263
fragment_99.0675 0.00353904728799314
loss_154.0575 0.00353904579497792
fragment_188.1075 0.00352826144964621
fragment_118.0525 0.00321192974691412
loss_70.0325 0.00319467687283729
fragment_417.1475 0.00300561153057131
fragment_127.0325 0.00298121302952362
fragment_130.0275 0.00290873094183323
loss_72.0575 0.00274787161459162
loss_45.0275 0.00272052446583763
loss_61.0525 0.00270666003481621
fragment_143.0575 0.00270305786034778
loss_69.0275 0.00263036145816449
fragment_202.0875 0.00262261929003027
loss_82.0175 0.00258189718701359
fragment_226.1425 0.00221122519678934
fragment_186.0625 0.00218210368627288
loss_167.9875 0.00218210368627288
fragment_158.0625 0.00217542184346953
fragment_211.0975 0.00211429407950394
fragment_228.1225 0.00208622046483692
fragment_387.2275 0.00202409913272742
fragment_360.1875 0.00197574482102578
loss_76.0425 0.00196909873234493
fragment_278.0075 0.00196401447831668
fragment_261.0025 0.00191555020988197
loss_93.0475 0.00191555020988197
loss_42.0225 0.00189045673648122
loss_126.1025 0.00186392882638648
loss_108.0925 0.00178186519422101
loss_71.0425 0.00166099799627879
fragment_259.0725 0.00157590708035101
fragment_234.0175 0.00156418426373032
loss_120.0325 0.00156418426373032
loss_43.0475 0.0015398360618896
fragment_507.1925 0.00145475534585299
fragment_222.1125 0.00145139183263953
loss_193.1125 0.00136338418025399
fragment_147.0825 0.00135536439774127
fragment_408.1625 0.00134571076941484
fragment_142.0575 0.00134554629761829
fragment_200.1075 0.00131984203339474
fragment_342.1825 0.00128513042790897
fragment_210.1125 0.00126765896631687
fragment_115.0375 0.00123650160839532

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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