Challenges, progress and promises of metabolite annotation for LC-MS-based metabolomics.

By R. Chaleckis, Isabel Meister, Pei Zhang, Craig E. Wheelock. Current Opinion in Biotechnology, 2019

299 citations recorded by the source corpus.

Accurate annotation is vital for data interpretation; however, metabolite identification is a major bottleneck in untargeted metabolomics. Although community guidelines for metabolite identification were published over a decade ago, adaptation of the recommended standards has been limited. The complexity of LC-MS data due to combinations of various chromatographic and mass spectrometric acquisition methods has resulted in the advent of diverse workflows, which often involve non-standardized manual curation. Herein, we review the parameters involved in metabolite reporting and provide a workflow to estimate the level of confidence in reported metabolite annotation. The future of metabolite identification will be heavily based upon the use of metabolome data repositories and associated data analysis tools, which will enable data to be shared, re-analyzed and re-annotated in an automated fashion.

Subfields: Biomedical Engineering, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/668fdcbf483e7d6a3af59e6a9f300fd670665de0

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In Biomedical Engineering

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Citations in the corpus

• Metabolic reaction network-based recursive metabolite annotation for untargeted metabolomics (2019, methodology): “We constructed four possible types of misannotated metabolites compared to seed metabolites, including (1) metabolites with mass error > 25 ppm, and RT error > 60 s; (2) metabolites with mass error < 25 ppm, but RT error > 60 s; (3) metabolites with mass error < 25 ppm and RT error < 60 s; (4) metab”
• MetaboAnalystR 3.0: Toward an Optimized Workflow for Global Metabolomics (2020, background): “This process is very time consuming and remains a key bottleneck in global metabolomics [19,20].”