SNOW

SNOW performs statistical analysis and visualization of protein-protein interaction networks by mapping protein or gene identifiers onto an interactome scaffold.

Key Features:

• Input Handling: SNOW accepts collections of protein or gene identifiers and maps them onto an interactome scaffold representing known molecular interactions.
• Network Analysis: SNOW computes node-level metrics including connectivity (number of direct interactions), betweenness, and clustering coefficient to quantify local network properties.
• Network Structure: SNOW determines components and bicomponents and identifies articulation points to characterize global and macroscopic network connectivity.
• Statistical Significance: SNOW estimates the statistical significance of calculated network parameters to provide quantitative confidence measures.
• Interactive Visualization: SNOW provides an interactive network viewer to visualize and explore resulting protein-protein interaction networks.

Scientific Applications:

• Cellular Biology: SNOW aids investigation of protein interaction patterns underlying cellular processes.
• Systems Biology: SNOW enables systems-level analysis of molecular interaction networks.
• Bioinformatics: SNOW provides statistical network metrics useful for bioinformatics analyses of interactomes.
• Disease Mechanisms: SNOW supports study of disease mechanisms by identifying altered network properties associated with pathology.
• Therapeutic Target Identification: SNOW supports identification of potential therapeutic targets by highlighting critical nodes and articulation points.

Methodology:

SNOW maps input protein or gene identifiers onto a reference interactome scaffold and computes network metrics (connectivity, betweenness, clustering coefficient, components, bicomponents, articulation points) and estimates their statistical significance.