IDSL.UFA Assigns High-Confidence Molecular Formula Annotations for Untargeted LC/HRMS Data Sets in Metabolomics and...

Area of Science:

• Metabolomics and Exposomics
• Computational Chemistry
• Bioinformatics

Background:

• Untargeted liquid chromatography/high-resolution mass spectrometry (LC/HRMS) is crucial for characterizing small molecules in biospecimens.
• Accurate annotation of LC/HRMS peaks with molecular formulas is essential for maximizing biological insights.
• Existing methods for molecular formula assignment can be limited in accuracy and scope.

Purpose of the Study:

• To develop and validate an R package, Integrated Data Science Laboratory for Metabolomics and Exposomics-United Formula Annotation (IDSL.UFA), for accurate molecular formula assignment.
• To improve the annotation of untargeted LC/HRMS data for metabolomics and exposomics studies.
• To facilitate the discovery of novel metabolites and environmental compounds in human samples.

Main Methods:

• Development of the IDSL.UFA R package for generating formula sources and theoretical isotopic profiles.
• Implementation of optimized ranking strategies for formula hit lists, both individually and aligned.
• Validation using untargeted metabolomics datasets and application to a pregnancy metabolome study.

Main Results:

• IDSL.UFA achieved 54.31-85.51% molecular formula assignment as the top hit and 90.58-100% within the top five hits in validation tests.
• Annotations were supported by tandem mass spectrometry data.
• Application to a pregnancy metabolome study identified hundreds of new compounds and suggested the presence of chlorinated perfluorotriether alcohols (Cl-PFTrEAs).

Conclusions:

• IDSL.UFA significantly improves molecular formula assignment accuracy in untargeted LC/HRMS data.
• The package is valuable for human metabolomics and exposomics research, minimizing the loss of biological insights.
• IDSL.UFA is publicly available on CRAN with comprehensive documentation.