ccNetViz

ccNetViz renders large and complex biological networks using WebGL in a JavaScript library to enable efficient visualization and analysis of protein-protein interaction maps, gene regulatory networks, and metabolic pathways.

Key Features:

• GPU-Accelerated Rendering: Leverages WebGL for high-speed rendering of large-scale networks to maintain performance on extensive datasets.
• Implementation: Provided as a JavaScript library that exposes programmatic control over network rendering and layout.
• Styling Syntax: Supports styling of nodes and edges via a CSS-like syntax for reproducible visual encodings.
• Dynamic Network Representation: Includes node and edge animations and supports dynamic changes to network state for temporal visualization.
• Analytical Tools: Integrates built-in analytical functions and supports defining properties a priori or importing properties from models and simulations.
• Force-Directed Layouts and Curved Edges: Implements force-directed layout algorithms and curved edge rendering to clarify topology and reduce visual overlap.
• Node Statistics: Computes node-level statistics to quantitatively characterize network components and interactions.

Scientific Applications:

• Systems Biology: Visualization and analysis of protein-protein interaction networks, gene regulatory networks, and metabolic pathway networks for interpretation of complex biological systems.

Methodology:

GPU-accelerated rendering via WebGL; force-directed layout algorithms combined with curved-edge rendering; node and edge animations for temporal representation; computation of node statistics; support for importing node/edge properties from models and simulations.