Automated Annotation of Untargeted All-Ion Fragmentation LC-MS Metabolomics Data with MetaboAnnotatoR

Untargeted metabolomics and lipidomics LC-MS experiments produce complex datasets, usually containing tens ofthousands of features from thousands of metabolites whose annotation requires additional MS/MS experiments and expertknowledge. All-ion fragmentation (AIF) LC-MS/MS acquisition provides fragmentation data at no additional experimental timecost. However, analysis of such datasets requires reconstruction of parent-fragment relationships and annotation of the resultingpseudo-MS/MS spectra. Here, we propose a novel approach for automated annotation of isotopologues, adducts, and in-sourcefragments from AIF LC-MS datasets by combining correlation-based parent-fragment linking with molecular fragment matching.Our workflow focuses on a subset of features rather than trying to annotate the full dataset, saving time and simplifying the process.We demonstrate the workflow in three human serum datasets containing 599 features manually annotated by experts. Precision andrecall values of 82-92% and 82-85%, respectively, were obtained for features found in the highest-rank scores (1-5). These resultsequal or outperform those obtained using MS-DIAL software, the current state of the art for AIF data annotation. Further validationfor other biological matrices and different instrument types showed variable precision (60-89%) and recall (10-88%) particularlyfor datasets dominated by nonlipid metabolites. The workflow is freely available as an open-source R package, MetaboAnnotatoR,together with the fragment libraries from Github (https://github.com/gggraca/MetaboAnnotatoR).

Automated summary

This paper proposes a novel approach for automated annotation of isotopologues, adducts, and in-source fragments from untargeted metabolomics and lipidomics LC-MS datasets. The method combines correlation-based parent-fragment linking with molecular fragment matching. The workflow demonstrates high precision and recall values and outperforms current state-of-the-art software for AIF data annotation.