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Related Concept Videos

Mass Spectrometry: Overview01:19

Mass Spectrometry: Overview

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Mass spectrometry is an analytical technique used to determine the molecular mass and molecular formula of a compound. The basic principle of mass spectrometry is to generate ions from the analyte molecule and measure these ion abundances against their molecular mass.  One common type of ionization, known as electrospray ionization or EI, bombards the analyte molecules in the gas phase with high-energy electron beams. The electron beams displace an electron from the molecule and leave...
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Tandem Mass Spectrometry01:21

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Tandem mass spectrometry is a technique that uses multiple mass analyzers in series to obtain a higher selectivity and signal-to-noise ratio for the analyte. Instruments with multiple analyzers separated by an interaction cell enable secondary fragmentation and selected study of the fragment ions.
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An unknown compound can be established by identifying the molecular ion peak in the mass spectrum. The molecular ion peak is often weak or absent due to the predominance of fragmentation in high-energy electron beams. In such cases, a low-energy electron beam can be used to scan the spectrum to enhance the intensity of the molecular ion peak. Additionally, chemical ionization, field ionization, and desorption ionization spectra are used to obtain a relatively intense molecular ion peak.
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MALDI-TOF Mass Spectrometry01:19

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Mass spectrometry is a powerful characterization technique that can identify and separate a wide variety of compounds ranging from chemical to biological entities, based on their mass-to-charge ratio (m/z). The instruments that allow this detection, known as mass spectrometers, have three components: an ion source, a mass analyzer, and a detector. These spectrometers differ based on the nature of their ion source and analyzers.
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Large-Scale Multi-Omics Genome-Wide Association Studies Mo-GWAS: Guidelines for Sample Preparation and Normalization
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SMetaS: A Sample Metadata Standardizer for Metabolomics.

Parker Ladd Bremer1, Oliver Fiehn2

  • 1Department of Chemistry, University of California, Davis, CA 95616, USA.

Metabolites
|August 25, 2023
PubMed
Summary
This summary is machine-generated.

Standardizing sample metadata is crucial for metabolomics data comparison. The new Sample Metadata Standardizer (SMetaS) tool enhances data FAIRness by converting free text into standardized terms, improving data discovery and reuse.

Keywords:
FAIRmeta-analysismetadatarepositorystandardization

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Area of Science:

  • Metabolomics
  • Bioinformatics
  • Data Science

Background:

  • Metabolomics data comparison across studies is hindered by a lack of standardized sample metadata.
  • Existing standardization efforts have primarily focused on data acquisition, processing, and storage, neglecting crucial metadata descriptions.
  • Ontology-based descriptions of biological samples and study designs are essential for the utility of metabolomics databases.

Purpose of the Study:

  • To introduce a user-centric tool, SMetaS (Sample Metadata Standardizer), for automatic standardization of sample metadata in metabolomics.
  • To enhance the FAIRness (Findability, Accessibility, Interoperability, and Reusability) of metabolomics data, particularly for data reuse and meta-analyses.
  • To facilitate the discovery of comparable datasets for large-scale analyses like cross-species comparisons or metabolomic atlases.

Main Methods:

  • SMetaS combines a database, API, and frontend within a containerized environment.
  • It features a two-component user-centric design for metadata matrix creation and transformation.
  • Natural language terms are converted to fixed vocabulary terms using synonym matching and typographical fixing via an n-grams/nearest neighbors model.

Main Results:

  • The SMetaS tool successfully standardizes sample metadata, replacing free text with controlled vocabulary terms.
  • This standardization process is designed for simplicity and guided by intelligent text processing strategies.
  • The tool enables downstream analysis through string equality, supporting FAIR retrospective data use.

Conclusions:

  • Automated sample metadata standardization is critical for advancing metabolomics data integration and analysis.
  • SMetaS significantly improves the FAIRness of metabolomics data, enabling more robust data reuse and discovery.
  • The tool's user-centric design and automated processing facilitate broader adoption and application in metabolomics research.