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Related Experiment Video

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MetaboKit: a comprehensive data extraction tool for untargeted metabolomics.

Pradeep Narayanaswamy1, Guoshou Teo2, Jin Rong Ow3

  • 1Sciex, R&D, Singapore.

Molecular Omics
|June 11, 2020
PubMed
Summary

MetaboKit is a new software for identifying and quantifying compounds in mass spectrometry untargeted metabolomics. It improves specificity and sensitivity in compound identification, offering accurate quantification for better biological insights.

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Area of Science:

  • Biochemistry
  • Computational Biology
  • Analytical Chemistry

Background:

  • Untargeted metabolomics using mass spectrometry is crucial for understanding biological systems.
  • Accurate compound identification and quantification are essential for reliable metabolomic data.
  • Existing software packages often face challenges with specificity and sensitivity in compound analysis.

Purpose of the Study:

  • To develop MetaboKit, a comprehensive software package for compound identification and relative quantification in mass spectrometry-based untargeted metabolomics.
  • To enhance the specificity and sensitivity of compound identification using MS and MS/MS data.
  • To provide accurate quantitative analysis for metabolomic studies.

Main Methods:

  • Development of MetaboKit software incorporating customized spectral libraries with retention time information.
  • Utilizing data dependent acquisition (DDA) and data independent acquisition (DIA) analysis.
  • Implementing a stringent identification algorithm requiring matches by both MS and MS/MS data.
  • MS/MS-based screening of in-source fragments (ISFs) to reduce unverifiable identifications.

Main Results:

  • MetaboKit demonstrated significantly greater specificity without loss in sensitivity compared to competing software.
  • The quantification module showed high correlation between peak area values and known concentrations in metabolite standards.
  • Analysis of mouse liver samples successfully identified lipid species and their ISFs, and quantitatively characterized a fatty liver phenotype.
  • MS1 and MS2 level data in DIA provided similar fold change estimates, with MS2 data enabling quantification of noisy precursor ion chromatograms.

Conclusions:

  • MetaboKit provides a robust and specific solution for compound identification and quantification in untargeted metabolomics.
  • The software's ability to integrate retention time and MS/MS data improves identification accuracy.
  • MetaboKit facilitates reliable quantitative analysis, enabling deeper biological insights from metabolomic studies.