Netview

Netview infers and enables exploration of gene regulatory networks in human and mouse by analyzing discretized gene expression profiles, computing mutual information (MI) to identify significant interactions, constructing Boolean adjacency matrices, clustering subnetworks, and supporting gene-specific queries by identifiers for subnetwork extraction and functional interpretation.

Key Features:

• Gene Network Inference: Infers gene regulatory relationships from gene expression profiles to generate candidate interaction networks.
• Data Integration and Discretization: Gathers gene expression profiles from public repositories and discretizes these profiles for downstream analysis.
• Mutual Information (MI) Analysis: Computes mutual information (MI) scores between gene pairs to quantify statistical dependency and identify potential interactions.
• Pruning of Insignificant Interactions: Removes gene pairs with insignificant MI scores through defined pruning steps to retain high-confidence relationships.
• Boolean Adjacency Matrix Construction: Encodes retained significant gene relationships into a Boolean adjacency matrix for network representation.
• Clustering and Module Identification: Applies clustering algorithms to the adjacency matrix to partition the network into modules or communities.
• Gene-focused Subnetwork Querying: Allows queries by gene identifiers to extract and focus analysis on specific subnetworks within broader networks.
• Functional and Graphical Representation: Associates inferred interactions with enriched Gene Ontology (GO) terms and provides graphical representations of subnetworks.

Scientific Applications:

• Hypothesis Generation: Enables generation of hypotheses about gene functions and regulatory mechanisms based on inferred networks and modules.
• Gene Signature Analysis: Supports examination of gene signatures for insights into disease mechanisms and potential biomarkers.
• Functional Annotation: Facilitates annotation of genes within network contexts using enriched GO terms to interpret biological processes and molecular functions.

Methodology:

Collect gene expression profiles from public databases; discretize profiles; calculate mutual information (MI) between genes; prune insignificant interactions; form a Boolean adjacency matrix of retained gene pairs; apply clustering algorithms to identify network modules; perform GO enrichment and generate graphical representations; allow gene-identifier queries to extract subnetworks.