Identifying and quantifying metabolites by scoring peaks of GC-MS data

Area of Science:

• Metabolomics
• Computational Biology
• Mass Spectrometry

Background:

• Metabolomics is a rapidly advancing omics field with applications in food science, nutrition, drug discovery, and systems biology.
• Gas chromatography-mass spectrometry (GC-MS) is widely used for metabolomics data acquisition.
• Current tools like AMDIS for GC-MS data analysis suffer from high false-positive rates and lack high-throughput capabilities.

Purpose of the Study:

• To develop a reliable and efficient computational tool for the identification and quantification of metabolites in GC-MS data.
• To address the limitations of existing software, particularly the high false-positive rates and lack of high-throughput analysis in AMDIS.
• To introduce a novel algorithm, PScore, for scoring metabolite peaks based on mass spectral library matching.

Main Methods:

• Implementation of the PScore algorithm within an R package named MetaBox.
• Evaluation of MetaBox by comparing its performance against AMDIS using standard metabolite mixtures.
• Analysis of volatile organic compounds (VOCs) from female and male mice fecal samples to assess biomarker discovery potential.

Main Results:

• MetaBox, utilizing the PScore algorithm, demonstrated lower percentages of false positives and false negatives compared to AMDIS.
• The MetaBox package facilitated the identification of a higher number of potential biomarkers associated with the metabolism of female and male mice.
• MetaBox provides a user-friendly interface for high-throughput metabolomics data analysis.

Conclusions:

• MetaBox offers a robust and efficient solution for metabolite identification and quantification in GC-MS based metabolomics.
• The developed R package enables the construction of flexible analysis pipelines for high-throughput metabolomics data.
• This tool addresses critical bottlenecks in GC-MS metabolome analysis, improving accuracy and efficiency.