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xMSannotator: An R Package for Network-Based Annotation of High-Resolution Metabolomics Data.
Karan Uppal1, Douglas I Walker1,2, Dean P Jones1
1Clinical Biomarkers Laboratory, Department of Medicine, Emory University , Atlanta, Georgia 30308, United States.
Analytical Chemistry
|December 16, 2016
Summary
An automated computational framework, xMSannotator, enhances chemical identification in mass spectrometry data. This tool improves the analysis of complex biological samples, enabling more accurate metabolite annotation for research.
Area of Science:
- Metabolomics
- Computational Chemistry
- Analytical Chemistry
Background:
- High-resolution mass spectrometry generates vast amounts of data, creating a bottleneck in chemical identification.
- Current analytical technologies can detect thousands of signals, but efficient utilization of raw data, especially for low-abundance metabolites, remains challenging.
Purpose of the Study:
- To develop an automated computational framework for annotating ions with possible chemical identities.
- To improve the identification of metabolites in complex biological samples using mass spectrometry data.
Main Methods:
- Development of a multistage clustering algorithm integrating metabolic pathway associations, intensity profiles, retention time, mass defect, and isotope/adduct patterns.
- Utilizing high-resolution mass spectrometry data from multiple samples and public databases (e.g., ChemSpider, KEGG, HMDB) for annotation.
- Incorporation of the algorithm into an R package named xMSannotator.
Main Results:
- The xMSannotator algorithm achieved an F1-measure of 0.8 on a dataset with known targets.
- Demonstrated robustness in scenarios where database size exceeds the actual number of metabolites.
- MS/MS evaluation showed 80% of high/medium confidence annotations were consistent with ion dissociation patterns in a human metabolomics dataset.
Conclusions:
- The xMSannotator framework provides an effective solution for the bottleneck in chemical identification within mass spectrometry-based metabolomics.
- The R package facilitates querying of diverse chemical and metabolic databases, enhancing the annotation of complex datasets.
- This computational tool significantly advances the capability to identify and analyze metabolites, particularly low-abundance ones.
