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IDSL.GOA: gene ontology analysis for interpreting metabolomic datasets
Priyanka Mahajan1, Oliver Fiehn2, Dinesh Barupal3
1Integrated Data Science Laboratory for Metabolomics and Exposomics, Department of Environmental Medicine and Public Health, Icahn School of Medicine at Mount Sinai, New York, 10954, USA.
Scientific Reports
|January 14, 2024
Summary
Biological pathway analysis for metabolomics is limited by database variations. We introduce IDSL.GOA, a tool enabling Gene Ontology (GO) analysis for metabolites, enhancing biological interpretation beyond traditional pathway maps.
Area of Science:
- Metabolomics
- Bioinformatics
- Systems Biology
Background:
- Metabolomic data interpretation often relies on pathway analysis, but database inconsistencies can lead to missed biological insights.
- Gene Ontology (GO) analysis is standard for gene, transcript, and protein data but has been lacking for metabolomics.
Purpose of the Study:
- To develop a novel knowledgebase and online tool, IDSL.GOA, for performing GO over-representation analysis on metabolite lists.
- To provide a standardized and comprehensive approach for interpreting metabolomic datasets using GO terms.
Main Methods:
- Creation of a comprehensive knowledgebase (KB) integrating metabolic GO terms, genes, EC annotations, and metabolites.
- Development of the IDSL.GOA online tool for executing GO over-representation analysis on user-provided metabolite lists.
- Application of the IDSL.GOA tool to a case study analyzing the metabolome of older versus young female brain cortex.
Main Results:
- The IDSL.GOA KB contains 2393 metabolic GO terms, 3144 genes, 1492 EC annotations, and 2621 metabolites.
- Analysis of the brain cortex metabolome identified 82 significantly overrepresented GO terms (FDR < 0.05).
- IDSL.GOA uncovered key metabolic processes not previously identified in other pathway databases.
Conclusions:
- GO analysis offers a complementary and more comprehensive approach to traditional pathway mapping for metabolomic data interpretation.
- The IDSL.GOA tool facilitates accurate and thorough analysis of metabolite pathway data, improving biological understanding.
- IDSL.GOA enhances the biological interpretation of metabolomic datasets by leveraging standardized GO term analysis.
