iMet: A Network-Based Computational Tool To Assist in the Annotation of Metabolites from Tandem Mass Spectra

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 allows 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.

High‐Spatial Resolution Mass Spectrometry Imaging: Toward Single Cell Metabolomics in Plant Tissues

“…Some serious bottlenecks need to be overcome, however, to achieve metabolomics scale MSI, which include robust identification of unknown metabolites and further development of new matrices to allow better visualization of more classes of compounds. In terms of metabolite identification, recently there has been a surge in tool developments such as MetFrag, iMet, CFM‐ID, and MS‐FINDER, among others, that are searchable based on in silico MS/MS fragmentation which will prove useful for confident metabolite identifications . Identification of unknown metabolites, however, still remains a daunting task, but recent efforts to standardize reporting in metabolite annotation is a crucial milestone to this end .…”

“…In terms of metabolite identification, recently there has been a surge in tool developments such as MetFrag, iMet, CFM-ID, and MS-FINDER, among others, that are searchable based on in silico MS/MS fragmentation which will prove useful for confident metabolite identifications. [67][68][69][70] Identification of unknown metabolites, however, still remains a daunting task, but recent efforts to standardize reporting in metabolite annotation is a crucial milestone to this end. [71] Along with various innovations in genomics technologies, high-spatial resolution MSI will revolutionize our understanding of the cooperative and antagonistic effects among metabolites, which are programmed by the genetics of multicellular organisms, by revealing unprecedented details of metabolic biology.…”

Review of emerging metabolomic tools and resources: 2015–2016

“…iMet is a Web‐based computational tool based on experimental MS/MS that allows annotation of unknown metabolites through the use of MS/MS spectra that identifies metabolites structurally similar to an unknown metabolite via giving a net atomic addition or removal that converts the known metabolite into the unknown one .…”

MESSAR: Automated recommendation of metabolite substructures from tandem mass spectra

“…The main reason is that 12 current MS/MS databases (spectral libraries) only contain a limited number of historical 13 spectra, far below the number of metabolites in reality [3,4]. 14 15 space that can be examined and have resulted in an improvement of the identification 16 accuracy by using massive molecular databases (for example, PubChem currently contains 17 over 100 million compounds [5]). These tools start by filtering the molecular database 18 using the precursor m/z of the unknown spectra, yielding up to thousands of structure 19 candidates.…”

“…The basic concept for 35 this strategy is that metabolites often share substructures, resulting in similar patterns 36 in their MS/MS spectra. Typical spectral features are product ions, neutral losses, or 37 mass differences [14][15][16][17]. 38 One important tool to explore spectral similarity is the Global Natural Products 39 Social Molecular Networking (GNPS) resource [18].…”