GraphWeb

GraphWeb performs graph-based analysis of biological networks to construct, mine, and interpret gene, protein, and microarray probeset interaction networks across eukaryotic genomes.

Key Features:

• Integration of Diverse Data: Integrates heterogeneous datasets from multiple species into comprehensive global networks encompassing genes, proteins, and microarray probesets, supporting directed and undirected as well as weighted and unweighted edges.
• Network Construction and Analysis: Constructs networks representing transcriptional regulation, protein interactions, and metabolic processes and supports analysis of individual or merged networks.
• Module Discovery: Detects gene and protein modules using a variety of algorithms and topological filters.
• Functional Interpretation: Annotates and interprets modules using Gene Ontology, pathway information, binding motifs, and microRNA target data.
• Cross-Species Analysis: Identifies conserved features across multiple species to enable comparative and evolutionary analyses.
• Mining Large Networks: Mines large-scale biological networks to extract smaller, significant modules and candidate connections within known pathways.
• Comparison of High-Throughput Datasets: Compares results derived from high-throughput datasets to aid validation and interpretation.

Scientific Applications:

• Systems Biology: Analysis of molecular interaction networks to elucidate cellular processes and regulatory mechanisms.
• Transcriptional and Post-transcriptional Regulation: Identification of transcriptional regulation modules and analysis of regulatory features such as binding motifs and microRNA targets.
• Protein Interaction and Metabolism Studies: Characterization of protein interaction modules and metabolic network components.
• Comparative Genomics: Detection of conserved network features across eukaryotic genomes for comparative and evolutionary studies.
• High-Throughput Data Interpretation: Integration and comparison of high-throughput datasets to validate findings and generate hypotheses.

Methodology:

Integrates heterogeneous multi-species datasets into directed/undirected and weighted/unweighted global networks; constructs and merges networks; applies a variety of algorithms and topological filters to detect gene and protein modules; annotates modules using Gene Ontology, pathways, binding motifs, and microRNA targets; searches for conserved features across species; mines large networks to extract significant modules; and compares outputs from high-throughput datasets.