PathDIP 5: improving coverage and making enrichment analysis more biologically meaningful

Area of Science:

• Bioinformatics
• Systems Biology
• Computational Biology

Background:

• Pathway enrichment analysis is crucial for understanding gene function but can suffer from bias and limited coverage.
• Existing pathway databases often lack integration, leading to fragmented data and reduced analytical power.

Purpose of the Study:

• To present PathDIP 5, an updated integrated pathway database designed to improve functional gene annotation and reduce bias in pathway enrichment analysis.
• To introduce enhanced analytical features for more interpretable and customizable pathway enrichment studies.

Main Methods:

• Integrated multiple pathway databases, including new sources PathBank and MetabolicAtlas, and removed outdated ones.
• Mapped all pathways to seven representative types and 53 ontology-based categories for structured analysis.
• Incorporated metabolite data alongside gene data and introduced API access for programmatic use.

Main Results:

• PathDIP 5 enables flexible enrichment analysis using combined or specific pathway subsets and source types.
• New features include knowledge-based pathway consolidation, drug-gene network analysis via Drugst.One, and improved result visualization with multiple charts.
• The database now includes both genes and metabolites, expanding its scope for biological interpretation.

Conclusions:

• PathDIP 5 provides a significantly enhanced platform for pathway enrichment analysis, offering greater interpretability and reduced bias.
• The integration of diverse data sources, flexible analysis options, and improved visualization tools empower researchers with deeper biological insights.