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arXiv · 1607.04122

iMet: A computational tool for structural annotation of unknown metabolites from tandem mass spectra

Antoni Aguilar-Mogas·Marta Sales-Pardo·Miriam Navarro·Ralf Tautenhahn·Roger Guimerà·Oscar Yanes
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Abstract

Untargeted metabolomic studies are revealing large numbers of naturally occurring metabolites that cannot be characterized because their chemical structures and MS/MS spectra are not available in databases. Here we present iMet, a computational tool based on experimental tandem mass spectrometry that could potentially allow the annotation of metabolites not discovered previously. iMet uses MS/MS spectra to identify metabolites structurally similar to an unknown metabolite, and gives a net atomic addition or removal that converts the known metabolite into the unknown one. We validate the algorithm with 148 metabolites, and show that for 89% of them at least one of the top four matches identified by iMet enables the proper annotation of the unknown metabolite. iMet is freely available at http://imet.seeslab.net.

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Antoni Aguilar-Mogas, Marta Sales-Pardo, Miriam Navarro, Ralf Tautenhahn, Roger Guimerà, Oscar Yanes. 2016-07-14. iMet: A computational tool for structural annotation of unknown metabolites from tandem mass spectra. https://doi.org/10.1021/acs.analchem.6b04512

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