MetaboliticsDB: A Database of Metabolomics Analyses.

Area of Science:

• Metabolomics
• Bioinformatics
• Systems Biology

Background:

• Existing metabolomics databases lack comprehensive analysis and result management tools.
• Current tools primarily superimpose metabolite data onto pathways, limiting deeper insights.
• There is a need for integrated platforms for storing, comparing, and analyzing metabolomics results.

Purpose of the Study:

• To introduce MetaboliticsDB, a database and analytics platform for metabolomics data.
• To enable storage, comparison, and advanced querying of metabolomics analysis results.
• To integrate genome-scale metabolic network analysis and AI-driven disease association.

Main Methods:

• Development of a web-based database for metabolomics analyses.
• Implementation of a genome-scale metabolic network-based analysis tool (Metabolitics) for flux analysis.
• Integration of an advanced querying interface and AI-based models for disease association.

Main Results:

• MetaboliticsDB stores analysis results for 2,174 individuals and 40 diseases.
• The platform provides biologically relevant metabolic network-level analysis.
• AI models achieve high accuracy in associating metabolomics data with diseases.
• Demonstrated utility via a Hepatocellular Carcinoma case study.

Conclusions:

• MetaboliticsDB offers a scalable architecture for comprehensive metabolomics data analysis.
• The platform facilitates the identification of shared mechanisms across different conditions.
• MetaboliticsDB enhances disease association studies through integrated AI and network analysis.