GenomeNet

GenomeNet provides integrated retrieval and network-based analysis of genomic, pathway, and chemical-reaction data to support reconstruction and interpretation of protein interaction networks and functional genomics/proteomics information.

Key Features:

• DBGET: A database retrieval system that searches and extracts entries from an extensive array of molecular biology databases.
• LinkDB: A linkage-search system that identifies connections between database entries to support construction of biological networks.
• KEGG PATHWAY: Curated protein interaction and pathway information representing cellular processes.
• KEGG LIGAND: Chemical reaction and small-molecule information linked to metabolic and signaling processes.
• KEGG GENES and SSDB: Resources used to reconstruct protein interaction networks for organisms with fully sequenced genomes.
• KEGG EXPRESSION and BRITE: Reference datasets for functional genomics (EXPRESSION) and hierarchical functional classification for proteomics (BRITE).
• Sequence and motif search integration: Inclusion of BLAST, FASTA, and MOTIF search capabilities for sequence-level analyses.
• Molecular visualization support: Compatibility with local molecular visualization applications such as RasMol.
• Graph representation and computations: Graph-based representations and computational methods for network modeling and simulation of higher-order biological functions.
• Similarity and biological-connection inference: Inclusion of similarity-based links and biological connections to support computational reasoning across datasets.

Scientific Applications:

• Pathway analysis: Use of PATHWAY and LIGAND data to analyze metabolic and signaling pathways.
• Interaction network reconstruction: Reconstruction of organism-specific protein interaction networks using GENES and SSDB.
• Functional genomics: Use of EXPRESSION as a reference for interpreting gene function and expression data.
• Proteomics and functional classification: Use of BRITE for hierarchical functional classification and interpretation of proteomic datasets.
• Network modeling and simulation: Application of graph representation and computations to simulate higher-order biological functions and network behavior.

Methodology:

Computational methods explicitly include database retrieval via DBGET, inter-database link searches via LinkDB, sequence searches using BLAST and FASTA, motif searches using MOTIF, molecular visualization with RasMol, KEGG curation of PATHWAY and LIGAND, reconstruction of protein interaction networks using GENES and SSDB, use of EXPRESSION and BRITE as reference datasets, and graph representation and computations for network modeling and similarity/biological-connection inference.