MCODE

MCODE detects densely connected regions in protein-protein interaction networks to identify potential molecular complexes and analyze biomolecular interaction connectivity within Cytoscape.

Key Features:

• Graph-theoretic clustering algorithm: Employs vertex weighting based on local neighborhood density and initiates outward traversal from locally dense seed proteins to identify dense clusters.
• Directed mode: Provides a directed mode to fine-tune specific clusters and examine cluster interconnectivity without processing the entire network.
• Robustness to false positives: Is designed to reduce the impact of false positives arising from high-throughput interaction techniques when identifying dense regions.
• Connectivity-based analysis: Operates using connectivity data alone to detect molecular complexes within protein-protein interaction networks.

Scientific Applications:

• Protein complex identification: Identifies regions within protein-protein interaction networks that correspond to potential molecular complexes.
• Biomolecular interaction network analysis: Facilitates analysis of network topology and cluster interconnectivity in biomolecular interaction datasets.
• Validation and benchmarking: Has been validated using protein interaction and complex information from Saccharomyces cerevisiae.

Methodology:

Uses a graph-theoretic clustering approach with vertex weighting by local neighborhood density and outward traversal from high-density seed proteins, with an optional directed mode.