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A Strategy for Sensitive, Large Scale Quantitative Metabolomics
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Bquant - Novel script for batch quantification of LCMS data.

Marko Rožman1, Mira Petrović2

  • 1Ruđer Bošković Institute, Bijenička cesta 54, 10000 Zagreb, Croatia.

Methodsx
|October 7, 2016
PubMed
Summary
This summary is machine-generated.

This study introduces Bquant, a Wolfram Mathematica script for automated batch quantification of liquid chromatography-mass spectrometry (LCMS) data. It significantly speeds up data post-processing, making complex analyses more efficient.

Keywords:
Automated routineComputer scriptComputer script for batch quantification of liquid chromatography mass spectrometry dataData post processingEmerging contaminantsLiquid chromatographyMass spectrometryMetabolitesQuantification

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Area of Science:

  • Analytical Chemistry
  • Biochemistry
  • Environmental Science

Background:

  • Quantitative target analysis using liquid chromatography-mass spectrometry (LCMS) is crucial in environmental, metabolomic, and toxicological research.
  • LCMS enables simultaneous determination of numerous analytes, but data post-processing for quantification can be time-consuming and software-dependent.

Purpose of the Study:

  • To develop an automated solution for batch quantification of LCMS data, addressing the limitations of manual processing and commercial software dependencies.
  • To facilitate faster and more efficient data analysis in scientific studies utilizing LCMS.

Main Methods:

  • Development of a Wolfram Mathematica script (Bquant) for automated batch quantification of LCMS data.
  • The script processes direct outputs from instrument control software or custom integration algorithms.
  • Validation of the script using diverse datasets, with working examples provided.

Main Results:

  • Bquant offers a simple and automated routine for batch-mode quantification of LCMS data.
  • Significant improvements in processing time were observed compared to manual interpretation.
  • The script allows for rapid re-analysis of data using different input parameters.

Conclusions:

  • The Bquant script effectively streamlines the quantification of LCMS data, overcoming common bottlenecks in post-processing.
  • This tool enhances the efficiency and accessibility of quantitative LCMS analysis across various scientific disciplines.