CPVA: a web-based metabolomic tool for chromatographic peak visualization and annotation

Area of Science:

• Analytical Chemistry
• Biochemistry
• Bioinformatics

Background:

• Non-targeted metabolomics using liquid chromatography-mass spectrometry (LC-MS) is crucial for analyzing complex biological samples.
• Popular software often generates false-positive peaks, compromising the reliability of metabolite measurements.
• Accurate identification of metabolite signals is essential for robust scientific conclusions.

Purpose of the Study:

• To develop an interactive web tool, CPVA, for accurate visualization and annotation of peaks in non-targeted metabolomics data.
• To reduce false-positive peak calling and improve the overall quality of metabolomics datasets.
• To provide a user-friendly solution for identifying background noise and contaminants.

Main Methods:

• Developed CPVA, an interactive web tool utilizing a chromatogram-centric strategy.
• Implemented visualization of peak morphology metrics to analyze chromatographic peaks.
• Integrated functions for annotating adducts, isotopes, and contaminants.

Main Results:

• CPVA effectively visualizes and annotates detected peaks, aiding in accurate metabolite identification.
• The tool helps distinguish true metabolite signals from background noise and contaminants.
• CPVA significantly decreases false-positive and redundant peak calling, enhancing data quality.

Conclusions:

• CPVA is a valuable, free, and user-friendly tool for non-targeted metabolomics studies.
• The chromatogram-centric approach improves the reliability and accuracy of metabolite quantification.
• Enhanced data quality from CPVA supports more robust biological interpretations.