Power of mzRAPP-Based Performance Assessments in MS1-Based Nontargeted Feature Detection

Area of Science:

• Analytical Chemistry
• Biochemistry
• Computational Biology

Background:

• Nontargeted metabolomics and exposomics rely on accurate MS1-based feature tables.
• Inadequate feature detection parameters can lead to quantitative errors and flawed biological conclusions.
• Previously, methods for assessing feature table completeness and accuracy were lacking.

Purpose of the Study:

• To introduce mzRAPP, a novel computational tool for quality assurance in metabolomics.
• To enable automated assessment of feature table completeness and abundance accuracy.
• To improve the reliability of quantitative biological interpretations from metabolomics data.

Main Methods:

• mzRAPP generates benchmark peak lists using an internal set of known molecules within the dataset.
• The benchmark peak lists are utilized within an automated pipeline to evaluate feature tables.
• The approach complements existing quality assurance strategies like parameter optimization and false-positive removal.

Main Results:

• mzRAPP facilitates automated assessment of feature table completeness and abundance accuracy.
• As few as 10 benchmark molecules can provide representative performance metrics.
• The tool enhances the quantitative accuracy of metabolomics data analysis.

Conclusions:

• mzRAPP provides a robust method for quality assurance in nontargeted metabolomics.
• Automated assessment using benchmark molecules improves the reliability of quantitative biological insights.
• This approach is crucial for advancing exposomics and metabolomics research.