MarVis-Pathway: integrative and exploratory pathway analysis of non-targeted metabolomics data

Area of Science:

• Metabolomics
• Systems Biology
• Bioinformatics

Background:

• Metabolite identification is a bottleneck in non-targeted metabolomics.
• Expert knowledge and interactive tools are crucial for data analysis.
• Integrating transcriptomics data can aid metabolite identification through pathway reconstruction.

Purpose of the Study:

• Introduce MarVis-Pathway tool for metabolite identification using cross-omics data.
• Support metabolite identification via pathway annotation and enrichment analysis.
• Facilitate exploratory data analysis and omics data integration.

Main Methods:

• Developed MarVis-Pathway tool for cross-omics data integration and metabolite identification.
• Implemented pathway enrichment and meta-analysis framework.
• Integrated tool within MarVis-Suite for interactive omics data visualization and analysis.
• Mapped dataset features using ID, name, and accurate mass, incorporating adduct and isotope correction.

Main Results:

• Demonstrated MarVis-Pathway's utility in identifying metabolites and associated pathways.
• Confirmed upregulation of jasmonate biosynthesis pathway during Arabidopsis wound response using combined mass spectrometry and DNA microarray data.
• Showcased interactive filtering, combination, clustering, and visualization of omics datasets.

Conclusions:

• MarVis-Pathway effectively aids metabolite identification by annotating pathways from cross-omics data.
• The tool facilitates a deeper understanding of metabolic responses in biological systems.
• Jasmonate biosynthesis is a key pathway involved in Arabidopsis wound response.