SGMNS

Large-scale metabolite annotation is a bottleneck in untargeted metabolomics. Here, we present a structure-guided molecular network strategy (SGMNS) for deep annotation of untargeted ultra-performance liquid chromatography-high resolution mass spectrometry-based metabolomics data. SGMNS used a global connectivity molecular network (GCMN), which was constructed by the structural similarity of metabolites. When the annotation was performed, experimental MS/MS spectra of known metabolites as seeds were assigned to corresponding neighbor metabolites in GCMN as their “pseudo” spectra, and the propagation was performed by searching predicted retention times, MS1 and “pseudo” spectra against metabolite features. Then, the annotated metabolite features were used as new seeds for annotation propagation again. A total of 2,041 metabolites were annotated from a pooled biological sample, and the annotation accuracy was > 83% with RSD < 2%.