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 of thousands of
features from thousands of metabolites whose annotation requires additional
MS/MS experiments and expert knowledge. All-ion fragmentation (AIF)
LC–MS/MS acquisition provides fragmentation data at no additional
experimental time cost. However, analysis of such datasets requires
reconstruction of parent–fragment relationships and annotation
of the resulting pseudo-MS/MS spectra. Here, we propose a novel approach
for automated annotation of isotopologues, adducts, and in-source
fragments 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 and recall values of 82–92%
and 82–85%, respectively, were obtained for features found
in the highest-rank scores (1–5). These results equal or outperform
those obtained using MS-DIAL software, the current state of the art
for AIF data annotation. Further validation for other biological matrices
and different instrument types showed variable precision (60–89%)
and recall (10–88%) particularly for 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).