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.
@article{arxiv.1607.04122,
title = {iMet: A computational tool for structural annotation of unknown metabolites from tandem mass spectra},
author = {Antoni Aguilar-Mogas and Marta Sales-Pardo and Miriam Navarro and Ralf Tautenhahn and Roger Guimerà and Oscar Yanes},
journal= {arXiv preprint arXiv:1607.04122},
year = {2017}
}