GNARE: automated system for high-throughput genome analysis with grid computational backend.

Area of Science:

• Bioinformatics
• Computational Biology
• Genomics

Background:

• Exponential growth in genomic data necessitates scalable computational infrastructure.
• Public genomics databases offer vast information requiring advanced analysis.
• Existing technologies struggle with the scale of modern biological data.

Purpose of the Study:

• To develop a scalable computational system for high-throughput genome analysis.
• To integrate data acquisition, analysis, and storage for bioinformatics.
• To leverage Grid computing for large-scale biological data processing.

Main Methods:

• Development of the Genome Analysis Research Environment (GNARE) system.
• Utilizing distributed heterogeneous Grid computing resources (e.g., Grid2003, TeraGrid).
• Implementation of a "virtual data" model for workflow management and resource allocation.

Main Results:

• GNARE efficiently automates major genome analysis steps.
• The system supports high-throughput computations across distributed Grid resources.
• Interactive web access to results is provided through an integrated database.

Conclusions:

• GNARE provides an effective computational framework for data-driven bioinformatics.
• Grid technologies are crucial for enabling scalable and efficient genome analysis.
• The "virtual data" model facilitates transparent mapping of workflows to Grid resources.