MSEA: a web-based tool to identify biologically meaningful patterns in quantitative metabolomic data

Area of Science:

• Metabolomics
• Bioinformatics
• Systems Biology

Background:

• Gene Set Enrichment Analysis (GSEA) is crucial for transcriptomic data interpretation.
• No equivalent tool exists for analyzing metabolomic data patterns.
• Understanding metabolite concentration changes is vital for biological research.

Purpose of the Study:

• Introduce Metabolite Set Enrichment Analysis (MSEA), a web server for metabolomic data analysis.
• Enable identification and interpretation of metabolite concentration changes in a biological context.
• Provide a tool analogous to GSEA for the metabolomics field.

Main Methods:

• Developed a web-based server (MSEA) with a library of ~1000 predefined metabolite sets.
• Implemented three enrichment analyses: Overrepresentation Analysis (ORA), Single Sample Profiling (SSP), and Quantitative Enrichment Analysis (QEA).
• Supported custom metabolite sets, common name/synonym conversion, and database identifier mapping.

Main Results:

• MSEA facilitates the identification of coordinated changes in metabolite concentrations.
• The tool generates interpretable graphs and tables with hyperlinks to pathway and disease information.
• MSEA can detect subtle but coordinated metabolite alterations missed by conventional methods.

Conclusions:

• MSEA provides a valuable resource for metabolomic data analysis, analogous to GSEA in transcriptomics.
• The tool enhances the biological interpretation of metabolite concentration data.
• MSEA is freely accessible and supports diverse metabolomic study types.