Experimental and computational approaches for deep metabolome annotation with application to the ecotoxicological model organism Daphnia magna

Identifying metabolomes with greater coverage and confidence is a critical step towards interpreting the metabolic basis of human and environmental health. Yet for over two decades the field of metabolomics has been inhibited by limited metabolite identification. To address this, we developed the Deep Metabolome Annotation (DMA) workflow. Applied to the ecological sentinel species, Daphnia magna, one pooled sample comprising ten distinct genotypes exposed to two environment conditions was systematically physicochemically-separated via solid-phase extraction, liquid- and gas-chromatography prior to extensive mass spectrometric (MS) fragmentation, generating >8000 data files, and supplemented by nuclear magnetic resonance spectroscopy. An extensive Galaxy-based computational approach was built to analyse these data, comprising over 30 tools. DMA generated >8,000 polar metabolite and lipid annotations in D. magna, with the data, tools and annotations disseminated freely via public data repositories and a custom web-based interface to ensure reusability and transferability.