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Updated: Jan 11, 2026

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

Adam M Tisch1, Jason M Inman1, Ewy A Mathé1

  • 1Division of Preclinical Innovation, National Center for Advancing Translational Sciences (NCATS), Rockville, Maryland 20850, United States.

Journal of Proteome Research
|November 17, 2025
PubMed
Summary
This summary is machine-generated.

Untargeted metabolomics compound identification is streamlined by metScribeR, an R package for rapid creation of retention time (RT) and m/z libraries. This tool accelerates metabolite standard library building for biological data interpretation.

Keywords:
LC-MSRmass spectrometrymetabolite identificationmetabolite librariesmetabolomicssoftware

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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.