PaintOmics 4: new tools for the integrative analysis of multi-omics datasets supported by multiple pathway databases

Area of Science:

• Bioinformatics
• Systems Biology
• Computational Biology

Background:

• Integrative analysis of multi-omics datasets is crucial for understanding complex biological systems.
• Existing tools often have limitations in analyzing metabolite data and regulatory layers.

Purpose of the Study:

• To introduce PaintOmics 4, an updated web server for integrative multi-omics analysis and visualization.
• To expand pathway analysis capabilities with new databases and advanced metabolite and regulatory omics modules.

Main Methods:

• Integration of three major pathway databases: KEGG, Reactome, and MapMan.
• Implementation of novel metabolite analysis methods: metabolite hub analysis and metabolite class activity analysis.
• Inclusion of a regulatory omics module for analyzing microRNA, transcription factor, and RNA-binding protein contributions.

Main Results:

• PaintOmics 4 supports comprehensive pathway knowledge for animals and plants.
• New methods identify key metabolites and metabolic classes regulated in experiments.
• The regulatory omics module reveals insights into trans-regulatory layer impacts on pathways.
• Demonstrated utility on mouse and plant datasets, yielding novel biological insights.

Conclusions:

• PaintOmics 4 significantly advances multi-omics data analysis and visualization.
• The enhanced features provide novel insights into regulatory biology and gene expression.
• The web server facilitates a deeper understanding of biological pathways and regulatory networks.