ISMAGS

ISMAGS enumerates motif instances in graphs by applying the Index-Based Subgraph Matching Algorithm with General Symmetries (ISMAGS) to exploit motif symmetries and accelerate motif detection in biological networks.

Key Features:

• Index-Based Subgraph Matching Algorithm (ISMAGS): Enumerates all occurrences of a specified motif within a graph using an index-based subgraph matching approach.
• Symmetry optimization: Exploits motif symmetries to reduce computational complexity and improve search performance compared to traditional subgraph matching methods.
• Exhaustive enumeration: Ensures comprehensive detection of motif instances across the input network.
• Scalability for large networks: Enables efficient analysis of large and complex biological networks, including protein-protein interaction networks, gene regulatory networks, and metabolic pathways.

Scientific Applications:

• Motif discovery in large-scale networks: Enumeration of recurring subgraph patterns to identify network motifs in biological datasets.
• Network topology analysis: Characterization and comparison of network structure through motif frequency and distribution.
• Functional annotation: Association of network elements with functional roles based on motif contexts.
• Systems biology investigations: Study of how specific motifs contribute to overall network behavior and functionality.

Methodology:

Applies the Index-Based Subgraph Matching Algorithm with General Symmetries (ISMAGS) to systematically search graphs for all occurrences of a given motif by accounting for the motif's symmetrical properties.