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Related Concept Videos

Proteomics01:33

Proteomics

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A proteome is the entire set of proteins that a cell type produces. We can study proteomes using the knowledge of genomes because genes code for mRNAs, and the mRNAs encode proteins. Although mRNA analysis is a step in the right direction, not all mRNAs are translated into proteins.
Proteomics is the study of proteomes' function. It involves the large-scale systematic study of the proteome to denote the protein complement expressed by a genome. Scientist Mark Wilkins coined the term...
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A Strategy for Sensitive, Large Scale Quantitative Metabolomics
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A guide to reverse metabolomics-a framework for big data discovery strategy.

Vincent Charron-Lamoureux1,2, Helena Mannochio-Russo1,2, Santosh Lamichhane3

  • 1Collaborative Mass Spectrometry Innovation Center, Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California, San Diego, La Jolla, CA, USA.

Nature Protocols
|February 28, 2025
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Summary
This summary is machine-generated.

Reverse metabolomics uses tandem mass spectrometry (MS/MS) spectra to mine public data for phenotype-associated information. This approach aids in discovering metabolite functions and biological relevance from existing spectral data.

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Area of Science:

  • Metabolomics
  • Big Data Science
  • Bioinformatics

Background:

  • Untargeted metabolomics generates tandem mass spectrometry (MS/MS) data for metabolite identification and structural elucidation.
  • There is increasing interest in utilizing public MS/MS data repositories for metabolite discovery.
  • Traditional metabolomics focuses on predefined research questions, while reverse metabolomics explores existing data for novel associations.

Purpose of the Study:

  • To introduce and guide users through a reverse metabolomics workflow.
  • To demonstrate how MS/MS spectra can be used as search queries in public databases.
  • To uncover phenotype-relevant information associated with metabolites.

Main Methods:

  • Obtaining MS/MS spectra (Universal Spectrum Identifier).
  • Utilizing Mass Spectrometry Search Tool for database searches.
  • Employing the Reanalysis Data User Interface framework to link spectra with metadata.
  • Validating findings through data analysis and potentially molecular networking.

Main Results:

  • The described four-part process enables efficient data mining of public MS/MS repositories.
  • Demonstrated application using MS/MS spectra of phenylalanine-cholic acid, phenylalanine-C4:0, and histidine-C4:0.
  • Explored microbial producers and associations with health conditions and organ distribution using the Global Natural Products Social Molecular Networking framework.

Conclusions:

  • Reverse metabolomics offers a powerful strategy for extracting biological insights from large-scale MS/MS data.
  • This approach facilitates the discovery of metabolite functions, distributions, and organismal associations.
  • The outlined workflow provides a framework for re-analyzing existing metabolomics data for new discoveries.