KeyPathwayMiner 4.0: condition-specific pathway analysis by combining multiple omics studies and networks with...

Area of Science:

• Computational systems biology
• Bioinformatics
• Network analysis

Background:

• Network enrichment analysis is crucial for identifying aberrant biological networks.
• Traditional methods primarily use gene expression and protein-protein interaction (PPI) data.
• Emerging omics technologies necessitate methods for integrating diverse data types.

Purpose of the Study:

• To introduce KeyPathwayMiner version 4.0, a tool for multi-omics network analysis.
• To enhance the identification of aberrant biological networks by integrating diverse data sources.
• To provide a systems-level view of biological networks.

Main Methods:

• KeyPathwayMiner 4.0 directly combines multiple omics data types (e.g., gene expression, phosphorylation, epigenetics).
• Incorporates user-defined positive and negative gene lists to bias network searches.
• Implemented as a Cytoscape app for seamless integration with existing bioinformatics workflows.

Main Results:

• KeyPathwayMiner 4.0 enables the analysis of combined omics datasets, moving beyond single-data-type limitations.
• The tool facilitates the discovery of relevant subnetworks by allowing targeted searches based on prior biological knowledge.
• Novel visualization features are included, enhancing the interpretation of results.

Conclusions:

• KeyPathwayMiner 4.0 is a Cytoscape app designed for multi-omics subnetwork extraction.
• The tool is readily accessible via the Cytoscape App Store and a dedicated website.
• This advancement supports a more comprehensive understanding of biological networks through integrated data analysis.