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metID: an R package for automatable compound annotation for LC-MS-based data
Xiaotao Shen1, Si Wu1, Liang Liang1
1Department of Genetics, Stanford University School of Medicine, Stanford, CA 94304, USA.
Bioinformatics (Oxford, England)
|August 25, 2021
Summary
Accurate compound identification from LC-MS data is challenging. The new metID R package streamlines this process by integrating multiple databases for automated and reproducible analysis.
Area of Science:
- Analytical Chemistry
- Bioinformatics
- Computational Biology
Background:
- Compound annotation for LC-MS data, including untargeted metabolomics and exposomics, presents significant challenges.
- Existing tools are limited by restricted spectral information sources and lack automation, hindering efficient analysis.
Purpose of the Study:
- To develop an automated and streamlined R package for comprehensive compound annotation using LC-MS data.
- To integrate information from major spectral databases for enhanced accuracy and reproducibility.
Main Methods:
- Developed metID, an R package for compound annotation.
- Integrated data from multiple major spectral databases.
- Ensured the package is flexible, simple, powerful, and cross-platform compatible.
Main Results:
- metID enables fully automatic and reproducible compound annotation.
- The package combines information from all major databases for comprehensive analysis.
- A detailed tutorial and case study are provided to facilitate adoption.
Conclusions:
- metID offers a robust solution to the long-standing challenge of compound annotation in LC-MS-based research.
- The R package enhances the efficiency and reliability of metabolomics and exposomics data analysis.
