gaggle

gaggle integrates diverse bioinformatics applications and databases to enable data exchange and systems biology analyses.

Key Features:

• Core data types: Supports four fundamental data types—names, matrices, networks, and associative arrays—for representing and exchanging biological information.
• Inter-process integration: Facilitates data exchange between R and Gaggle-enabled applications such as Cytoscape, Firegoose, and the Gaggle Genome Browser.
• Semantic flexibility: Applies separation of concerns and classic software engineering principles to enable integration and extension of diverse bioinformatics programs and web resources.
• Comprehensive data coverage: Enables simultaneous analysis of mRNA and protein abundance, protein-protein and protein-DNA interactions, functional associations (operon and chromosomal proximity), metabolic pathways, and PubMed abstracts.
• Database and tool integration: Integrates databases such as KEGG, BioCyc, and STRING with tools including Cytoscape, DataMatrixViewer, the R statistical environment, and TIGR Microarray Expression Viewer.
• Runtime technologies: Employs Java RMI and Java Web Start for communication and deployment.

Scientific Applications:

• Systems biology integration: Combines heterogeneous datasets to support network, pathway, and expression analyses.
• Pathogenesis gene discovery: Has been applied to explore Helicobacter pylori pathogenesis genes and to identify a putative ricin-like protein.
• Cross-database hypothesis generation: Links interaction, pathway, expression, and literature data across KEGG, BioCyc, STRING, and PubMed to support hypothesis generation.

Methodology:

Uses four data types (names, matrices, networks, associative arrays) and implements data exchange between R and Gaggle-enabled applications (Cytoscape, Firegoose, Gaggle Genome Browser) using Java RMI and Java Web Start, following separation-of-concerns architecture.