foodMASST a mass spectrometry search tool for foods and beverages

West, Kiana; Schmid, Robin; Gauglitz, Julia M.; Wang, Mingxun; Dorrestein, Pieter C.

doi:10.1038/s41538-022-00137-3

Cited by 12 publications

(11 citation statements)

References 13 publications

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“…Additionally, other matches are related to the food production chain, such as feed additives to promote animal growth that are tagged as 'drug' , which include the antimicrobial agents monensin, enilconazole, kanamycin and other agricultural additives or environmental toxins (e.g. domoic acid) 23 .…”

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Enhancing untargeted metabolomics using metadata-based source annotation

et al. 2022

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Human untargeted metabolomics studies annotate only ~10% of molecular features. We introduce reference-data-driven analysis to match metabolomics tandem mass spectrometry (MS/MS) data against metadata-annotated source data as a pseudo-MS/MS reference library. Applying this approach to food source data, we show that it increases MS/MS spectral usage 5.1-fold over conventional structural MS/MS library matches and allows empirical assessment of dietary patterns from untargeted data.Complex sequence data from metagenomic (see Box 1 for definition of terms) or metatranscriptomic experiments require for interpretation both databases of curated genes and reference data, such as whole genomes or other sequence data with carefully curated metadata (developmental stage, tissue location, phenotype, etc.) [1][2][3][4] . Such reference data-driven (RDD) analysis increases understanding of complex communities by using matches between genes or transcripts of known and unknown origin. The RDD strategy is essential for the successful analysis of most metatranscriptomics or metagenomics data. By analogy, interpreting liquid chromatography-tandem mass spectromtery (LC-MS/MS)-based untargeted metabolomics data is performed by searching structural MS/MS libraries. However, leveraging reference data with curated and structured controlled vocabulary metadata to improve insights obtainable from untargeted MS/MS-based metabolomics is not yet done.RDD analysis uses not only annotated MS/MS-spectra but also all unannotated spectra. The gas chromatography-mass spectrometry (GC-MS) BinBase resource has made a step in the direction of RDD. With BinBase one can annotate if a spectrum match has been observed in a non-public GC-MS dataset. However, the metadata is not well controlled and lacks the ability to add contextualized metadata 5,6 . In addition, as we have previously demonstrated, using structural annotations, the source can be determined by literature mining 7 . However, owing to the above mentioned limitations and/ or inability to link related spectra in the case of metabolism, the above strategies to annotate unknowns cannot be used to systematically to interpret the source information at the dataset level. We therefore introduce the RDD approach for metabolomics (Fig. 1), followed by a use case demonstrating empirical food readouts from untargeted human data (Fig. 2).Untargeted MS/MS-based metabolomics experiments involve searching MS/MS structural libraries since the late 1970's 8,9 , or, more recently, for investigating the distribution of a MS/MS spectrum across public untargeted data 10 . Instead of only leveraging a single MS/MS spectrum to obtain an annotation, RDD metabolomics uses all MS/MS spectra from untargeted metabolomics files, which con-

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“…22 Department of Medicine, Duke University, Durham, NC, USA. 23 Duke Institute of Brain Sciences, Duke University, Durham, NC, USA. 24 Department of Neurosciences, University of California San Diego, La Jolla, CA, USA.…”

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Enhancing untargeted metabolomics using metadata-based source annotation

et al. 2022

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“…Cloud-based solutions, such as the GNPS analysis platform, have the potential to be extremely scalable while hiding this complexity from the user. For example, GNPS allows users to query their data against 1.2 billion open MS/MS spectra using the Mass Spectrometry Search Tool (MASST) to discover public datasets that contain similar MS/MS spectra (M. Wang et al, 2020;West et al, 2022). Developing and maintaining such platforms requires suitable, continued investments and a team willing to maintain the resources for the benefit of the community.…”

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The critical role that spectral libraries play in capturing the metabolomics community knowledge

Bittremieux

Wang

Dorrestein

2022

Preprint

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Background: Spectral library searching is currently the most common approach for compound annotation in untargeted metabolomics. Spectral libraries applicable to liquid chromatography mass spectrometry have grown in size over the past decade to include hundreds of thousands to millions of mass spectra and tens of thousands of compounds, forming an essential knowledge base for the interpretation of metabolomics experiments. Aim of Review: We describe existing spectral library resources, highlight different strategies for compiling spectral libraries, and discuss quality considerations that should be taken into account when interpreting spectral library searching results. Finally, we describe how spectral libraries are empowering the next generation of machine learning tools in computational metabolomics, and discuss several opportunities for using increasingly accessible large spectral libraries. Key Scientific Concepts of Review: This review focuses on the current state of spectral libraries for untargeted LC-MS/MS based metabolomics. We show how the number of entries in publicly accessible spectral libraries has increased more than 60-fold in the past eight years to aid molecular interpretation and we discuss how the role of spectral libraries in untargeted metabolomics will evolve in the near future.

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“…For further data analysis based on metadata, such as that performed with ReDU ( 12 ) and foodMASST ( 11 , 13 ), and for appropriate interpretation of the results, the sample metadata needs to be enriched. In FoodMR, we adopted the sample names, sample IDs, and category names that were defined in the Standard Tables of Food Composition in Japan-2015 (Seventh Revised Version).…”

Section: Discussionmentioning

confidence: 99%

“…It is even difficult to judge whether two given data are comparable by checking the analytical methods (metadata) and actual accuracy/resolution of the detector when the data are measured. Searches based on mass spectra and precursor ion mass, such as those provided by MASST ( 10 ), foodMASST ( 11 ), ReDU ( 12 ) and Metabolomics Workbench ( 9 ), are powerful tools for finding samples that may contain the queried metabolite. However, the absence and thus sample-specific localization of the queried metabolite cannot be examined using their datasets consisting of mixed conditions.…”

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The Thing Metabolome Repository family (XMRs): comparable untargeted metabolome databases for analyzing sample-specific unknown metabolites

Sakurai

Yamazaki

Suda

et al. 2022

Nucleic Acids Research

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The identification of unknown chemicals has emerged as a significant issue in untargeted metabolome analysis owing to the limited availability of purified standards for identification; this is a major bottleneck for the accumulation of reusable metabolome data in systems biology. Public resources for discovering and prioritizing the unknowns that should be subject to practical identification, as well as further detailed study of spending costs and the risks of misprediction, are lacking. As such a resource, we released databases, Food-, Plant- and Thing-Metabolome Repository (http://metabolites.in/foods, http://metabolites.in/plants, and http://metabolites.in/things, referred to as XMRs) in which the sample-specific localization of unknowns detected by liquid chromatography–mass spectrometry in a wide variety of samples can be examined, helping to discover and prioritize the unknowns. A set of application programming interfaces for the XMRs facilitates the use of metabolome data for large-scale analysis and data mining. Several applications of XMRs, including integrated metabolome and genome analyses, are presented. Expanding the concept of XMRs will accelerate the identification of unknowns and increase the discovery of new knowledge.

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foodMASST a mass spectrometry search tool for foods and beverages

Cited by 12 publications

References 13 publications

Enhancing untargeted metabolomics using metadata-based source annotation

Enhancing untargeted metabolomics using metadata-based source annotation

The critical role that spectral libraries play in capturing the metabolomics community knowledge

The Thing Metabolome Repository family (XMRs): comparable untargeted metabolome databases for analyzing sample-specific unknown metabolites

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