MetScribeR: A Semiautomated Tool for Data Processing of In-House LC-MS Metabolite Reference Libraries

Area of Science:

• Metabolomics
• Mass Spectrometry
• Bioinformatics

Background:

• Compound identification in untargeted metabolomics is crucial for biological data interpretation.
• Building in-house metabolite standard libraries with retention time (RT) data complements existing MS/MS spectral repositories.
• Current methods for creating these libraries are time-intensive and labor-intensive.

Purpose of the Study:

• To develop metScribeR, an R package and Shiny application, to accelerate the creation of retention time (RT) and m/z libraries for metabolite standards.
• To provide a user-friendly interface for processing mass spectrometry (MS) data, including peak finding, filtering, and quality control.
• To enable compound identification without requiring MS/MS spectral data, offering an identification probability estimate for each adduct.

Main Methods:

• Development of metScribeR, an R package with a Shiny application.
• Utilized peak finding, filtering, and quality review algorithms for MS data.
• Benchmarking against manual methods for RT and m/z library creation.

Main Results:

• metScribeR significantly reduces the effort required per standard to approximately 10 seconds.
• Achieved a high correlation (0.99) between manual and metScribeR-derived RTs, indicating high accuracy.
• Successfully filtered out poor-quality peaks and generated comprehensive output files with identity, m/z, RT, and quality information.

Conclusions:

• metScribeR offers an efficient and user-friendly solution for building metabolite standard libraries.
• The package facilitates accurate compound identification in untargeted metabolomics by leveraging RT and m/z data.
• metScribeR is open-source, promoting accessibility and further development in the field.