Recent advances in mass spectrometry-based computational metabolomics

Timothy M D Ebbels; Justin J J van der Hooft; Haley Chatelaine; Corey Broeckling; Nicola Zamboni; Soha Hassoun; Ewy A Mathé

doi:10.1016/j.cbpa.2023.102288

Recent advances in mass spectrometry-based computational metabolomics

Curr Opin Chem Biol. 2023 Jun:74:102288. doi: 10.1016/j.cbpa.2023.102288. Epub 2023 Mar 24.

Authors

Timothy M D Ebbels¹, Justin J J van der Hooft², Haley Chatelaine³, Corey Broeckling⁴, Nicola Zamboni⁵, Soha Hassoun⁶, Ewy A Mathé⁷

Affiliations

¹ Section of Bioinformatics, Department of Metabolism, Digestion & Reproduction, Imperial College London, Burlington Danes Building, Hammersmith Hospital, Du Cane Road, London W12 0NN, UK. Electronic address: t.ebbels@imperial.ac.uk.
² Bioinformatics Group, Wageningen University & Research, Wageningen 6708 PB, the Netherlands; Department of Biochemistry, University of Johannesburg, Auckland Park, Johannesburg 2006, South Africa.
³ Informatics Core, Division of Preclinical Innovation, National Center for Advancing Translational Sciences, Rockville, MD, USA.
⁴ Bioanalysis and Omics Center, Analytical Resources Core, Colorado State University, Fort Collins, CO, USA.
⁵ Institute of Molecular Systems Biology, ETH Zurich, Zurich, Switzerland.
⁶ Department of Computer Science, Tufts University, Medford, MA, USA; Department of Chemical and Biological Engineering, Tufts University, Medford, MA, USA.
⁷ Informatics Core, Division of Preclinical Innovation, National Center for Advancing Translational Sciences, Rockville, MD, USA. Electronic address: ewy.mathe@nih.gov.

PMID: 36966702
DOI: 10.1016/j.cbpa.2023.102288

Abstract

The computational metabolomics field brings together computer scientists, bioinformaticians, chemists, clinicians, and biologists to maximize the impact of metabolomics across a wide array of scientific and medical disciplines. The field continues to expand as modern instrumentation produces datasets with increasing complexity, resolution, and sensitivity. These datasets must be processed, annotated, modeled, and interpreted to enable biological insight. Techniques for visualization, integration (within or between omics), and interpretation of metabolomics data have evolved along with innovation in the databases and knowledge resources required to aid understanding. In this review, we highlight recent advances in the field and reflect on opportunities and innovations in response to the most pressing challenges. This review was compiled from discussions from the 2022 Dagstuhl seminar entitled "Computational Metabolomics: From Spectra to Knowledge".

Keywords: Benchmarking; Cheminformatics; Chemometrics; Machine learning; Metabolite identification; Metabolomics; Multi-omics; Small molecules; Visualization.

Recent advances in mass spectrometry-based computational metabolomics

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