APEX: an Annotation
Propagation Workflow through Multiple
Experimental Networks to Improve the Annotation of New Metabolite
Classes in Caenorhabditis elegans
posted on 2023-11-21, 01:04authored byLiesa Salzer, Elva María Novoa-del-Toro, Clément Frainay, Kohar Annie B Kissoyan, Fabien Jourdan, Katja Dierking, Michael Witting
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.