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Feature-Based Molecular Networking for Metabolite Annotation

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Computational Methods and Data Analysis for Metabolomics

Part of the book series: Methods in Molecular Biology ((MIMB,volume 2104))

Abstract

The Global Natural Product Social Molecular Networking (GNPS) platform leverages tandem mass spectrometry (MS/MS) data for annotation of compounds. Molecular networks aid in the visualization of the chemical space within a metabolomics experiment. Recently, molecular networking has been combined with feature detection methods to yield Feature-Based Molecular Networking (FBMN). FBMN allows for the discrimination of isomers within the molecular network, incorporation of quantitative information generated by the feature detection tools into visualization of the molecular network, and compatibility with forthcoming in silico annotation tools. This chapter provides step-by-step methods for generating a molecular network to annotate microbial natural products using the Global Natural Product Social Molecular Networking (GNPS) Feature-Based Molecular Networking (FBMN) workflow.

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Acknowledgments

We thank L. Dietrich (Columbia University) for kindly providing the P. aeruginosa strains and M. Wang (University of California, San Diego) for providing feedback on the manuscript. Our work on microbial metabolomics has been supported by the National Institutes of Health (National Institute of General Medical Sciences grants K01 GM103809 and R35 GM128690), the ALSAM Foundation (L.S. Skaggs Professorship and Therapeutical Innovation Award), and the University of Colorado.

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Authors and Affiliations

  1. Department of Pharmaceutical Sciences, Skaggs School of Pharmacy and Pharmaceutical Sciences, University of Colorado, Aurora, CO, USA

    Vanessa V. Phelan

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  1. Vanessa V. Phelan
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Correspondence to Vanessa V. Phelan .

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Editors and Affiliations

  1. Department of Medicine, Emory University School of Medicine, Atlanta, GA, USA

    Shuzhao Li

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Phelan, V.V. (2020). Feature-Based Molecular Networking for Metabolite Annotation. In: Li, S. (eds) Computational Methods and Data Analysis for Metabolomics. Methods in Molecular Biology, vol 2104. Humana, New York, NY. https://doi.org/10.1007/978-1-0716-0239-3_13

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  • DOI: https://doi.org/10.1007/978-1-0716-0239-3_13

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  • Publisher Name: Humana, New York, NY

  • Print ISBN: 978-1-0716-0238-6

  • Online ISBN: 978-1-0716-0239-3

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