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Salzer, L. ; Novoa-del-Toro, E.M.* ; Frainay, C.* ; Kissoyan, K.A.B.* ; Jourdan, F.* ; Dierking, K.* ; Witting, M.

APEX: An annotation propagation workflow through multiple experimental networks to improve the annotation of new metabolite classes in Caenorhabditis elegans.

Anal. Chem. 95, 17550–17558 (2023)
Publ. Version/Full Text DOI PMC
Open Access Gold (Paid Option)
Creative Commons Lizenzvertrag
Spectral similarity networks, also known as molecular networks, are crucial in non-targeted metabolomics to aid identification of unknowns aiming to establish a potential structural relation between different metabolite features. However, too extensive differences in compound structures can lead to separate clusters, complicating annotation. To address this challenge, we developed an automated Annotation Propagation through multiple EXperimental Networks (APEX) workflow, which integrates spectral similarity networks with mass difference networks and homologous series. The incorporation of multiple network tools improved annotation quality, as evidenced by high matching rates of the molecular formula derived by SIRIUS. The selection of manual annotations as the Seed Nodes Set (SNS) significantly influenced APEX annotations, with a higher number of seed nodes enhancing the annotation process. We applied APEX to different Caenorhabditis elegans metabolomics data sets as a proof-of-principle for the effective and comprehensive annotation of glycerophospho N-acyl ethanolamides (GPNAEs) and their glyco-variants. Furthermore, we demonstrated the workflow's applicability to two other, well-described metabolite classes in C. elegans, specifically ascarosides and modular glycosides (MOGLs), using an additional publicly available data set. In summary, the APEX workflow presents a powerful approach for metabolite annotation and identification by leveraging multiple experimental networks. By refining the SNS selection and integrating diverse networks, APEX holds promise for comprehensive annotation in metabolomics research, enabling a deeper understanding of the metabolome.
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Publication type Article: Journal article
Document type Scientific Article
Corresponding Author
ISSN (print) / ISBN 0003-2700
e-ISSN 1520-6882
Journal Analytical Chemistry
Quellenangaben Volume: 95, Issue: 48, Pages: 17550–17558 Article Number: , Supplement: ,
Publisher American Chemical Society (ACS)
Publishing Place 1155 16th St, Nw, Washington, Dc 20036 Usa
Non-patent literature Publications
Reviewing status Peer reviewed
Institute(s) CF Metabolomics & Proteomics (CF-MPC)
Research Unit Analytical BioGeoChemistry (BGC)
Grants NIH Office of Research Infrastructure Programs
German Science Foundation DFG
, Deutsche Forschungsgemeinschaft