MET-XAlign: a metabolite cross-alignment tool for LC/MS-based comparative metabolomics.

Area of Science:

• Analytical Chemistry
• Biochemistry
• Computational Biology

Background:

• Liquid chromatography/mass spectrometry (LC/MS) metabolite profiling is crucial for comparative metabolomics.
• Current LC/MS metabolomics faces challenges in aligning metabolite features across different runs and experiments.
• Variability in peak features and retention times complicates metabolite identification and comparison.

Purpose of the Study:

• To introduce MET-XAlign, a novel metabolite-based alignment approach for LC/MS metabolomics.
• To address the critical challenges of aligning metabolites across diverse LC/MS profiles and experimental conditions.
• To enable robust cross-alignment of known and unknown metabolites in comparative metabolomics.

Main Methods:

• Developed MET-XAlign, a metabolite-based alignment tool utilizing deduced molecular mass and estimated retention time.
• Leveraged information extracted by the MET-COFEA tool for metabolite alignment.
• Implemented the core algorithm in C++ for efficiency and the visualization interface in the Microsoft.NET Framework.

Main Results:

• MET-XAlign successfully cross-aligns metabolite compounds across different samples, biological experiments, and electrospray ionization modes.
• The approach effectively handles variations in peak features and retention times inherent in LC/MS data.
• Demonstrated the capability to align both known and unknown metabolite compounds.

Conclusions:

• MET-XAlign represents a significant advancement in LC/MS-based comparative metabolomics.
• The proposed metabolite-based cross-alignment approach overcomes key limitations in current metabolomics data analysis.
• MET-XAlign is an efficient and valuable tool for researchers in the field, with software freely available.