GpemDB: A Scalable Database Architecture with the Multi-omics Entity-relationship Model to Integrate Heterogeneous...

Area of Science:

• Agricultural Science
• Bioinformatics
• Genomics

Background:

• High-throughput sequencing and phenotyping enable multi-omics approaches for accelerated crop breeding.
• Data heterogeneity and lack of integrated databases hinder efficient multi-omics data utilization in breeding.
• Existing systems lack comprehensive support for end-to-end association analysis across diverse omics datasets.

Purpose of the Study:

• To propose a scalable entity-relationship model and database architecture for managing cross-platform multi-omics data.
• To facilitate the exploration of relationships among genomics, phenomics, and enviromics data.
• To accelerate crop breeding efficiency through integrated data management and analysis.

Main Methods:

• Development of a scalable entity-relationship model and database architecture.
• Normalization of crop omics data (genomics, phenomics, enviromics) for storage.
• Integration of statistical tools for agricultural analysis within the database.
• Case study using rice (Oryza sativa L.) to demonstrate data structure and management.

Main Results:

• Development of GpemDB, a general-purpose scalable database integrating genomics, phenomics, and enviromics data.
• GpemDB is the first database designed to manage these four omics data types concurrently.
• The database features metadata-level and informative-level layers with a visualized scheme for data management and analysis.

Conclusions:

• GpemDB architecture and model are effective for utilizing big data in crop research and breeding.
• Successful application in a rice population demonstrates the database's potential.
• The system offers a powerful tool for high-precision and efficient crop improvement.