Clust&See: a Cytoscape plugin for the identification, visualization and manipulation of network clusters.

Area of Science:

• Bioinformatics
• Computational Biology
• Network Science

Background:

• Analyzing large biological networks, like protein-protein interaction networks, presents significant challenges.
• Existing tools often struggle with the scale and complexity of whole-network analysis.

Purpose of the Study:

• To introduce Clust&See, a novel Cytoscape plugin designed for the comprehensive analysis of large biological networks.
• To facilitate the identification, visualization, and in-depth analysis of network clusters.

Main Methods:

• Development of a Cytoscape plugin, Clust&See.
• Integration of three advanced graph clustering algorithms.
• Visualization of network partitions as quotient graphs and clusters as subnetworks.

Main Results:

• Clust&See enables the application of multiple clustering algorithms to biological networks.
• The plugin visualizes cluster relationships and their organization within the broader network context.
• Tools are provided for detailed investigation of inter-cluster and intra-cluster relationships.

Conclusions:

• Clust&See offers a powerful solution for dissecting complex biological networks.
• The plugin enhances the understanding of network organization and modularity through advanced visualization and analysis.
• It supports researchers in exploring large-scale biological data more effectively.