CPA: a web-based platform for consensus pathway analysis and interactive visualization

Area of Science:

• Molecular Biology
• Genetics
• Bioinformatics

Background:

• A significant gap exists between high-throughput data generation and knowledge extraction in molecular biology and genetics.
• Living organisms are complex systems where phenotypes arise from intricate pathway interactions.
• Advanced, user-friendly pathway analysis tools are crucial for interpreting abundant biological data.

Purpose of the Study:

• To introduce Consensus Pathway Analysis (CPA), a novel web-based platform.
• To provide researchers with a versatile tool for pathway and meta-analysis.
• To facilitate the identification and exploration of biological pathways underlying specific conditions.

Main Methods:

• CPA integrates eight established pathway analysis methods: GSEA, GSA, FGSEA, PADOG, Impact Analysis, ORA/Webgestalt, KS-test, and Wilcox-test.
• The platform supports meta-analysis across multiple datasets.
• It accepts various input formats including gene lists, fold changes, expression matrices, and data from NCBI GEO.
• Analysis is supported for multiple organisms using KEGG and Gene Ontology databases.

Main Results:

• CPA enables the combined analysis of multiple methods and datasets for robust pathway identification.
• Users can interactively explore identified pathways and gene-pathway relationships.
• The platform facilitates a deeper understanding of the biological phenomena driving observed phenotypes.

Conclusions:

• Consensus Pathway Analysis (CPA) effectively bridges the gap between complex biological data and actionable insights.
• The platform offers a powerful and accessible solution for researchers in molecular biology and genetics.
• CPA enhances the ability to translate high-throughput data into a comprehensive understanding of biological systems.