Hubba

Hubba identifies essential hub nodes in protein interactomes (protein–protein interaction networks) to support analysis of biochemical pathways and prioritization of potential therapeutic targets.

Key Features:

• Graph-theory-based analysis: Analyzes interactomes using graph theory–based methodologies to detect network topological properties.
• Algorithms: Implements the Maximum Neighborhood Component (MNC) and Density of Maximum Neighborhood Component (DMNC) algorithms for hub detection.
• Input formats: Accepts PSI format (Proteomics Standards Initiative versions 2.5 and 1.0), tabular format, and tab-delimited files with weight values.
• Output: Provides node rankings by a composite index, a manifest graph illustrating relationships among identified hubs, and detailed output files.
• Cross-species applicability: Applies topology-based analysis to interactomes from organisms including yeast, rat, mouse, and human.

Scientific Applications:

• Essential protein identification: Identification and prioritization of essential proteins and hub nodes in interactomes, demonstrated in yeast where 80% of the top 10 ranked hubs and over 70% of the top 40 were reported as essential proteins.
• Pathway and target prioritization: Support for analysis of biochemical pathways and prioritization of potential drug targets relevant to cancer and infections caused by emerging pathogens.
• Comparative network analysis: Comparative topology-based analysis across species to explore conserved and organism-specific network hubs.

Methodology:

Employs graph theory–based methodologies, implements MNC and DMNC algorithms, assigns node ranks via a composite index, and produces a manifest graph along with detailed result files.