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

Liang Gong1, Qiaojun Lou2, Chenrui Yu1

  • 1School of Mechanical Engineering, Shanghai Jiao Tong University, 200240 Shanghai, China.

Frontiers in Bioscience (Landmark Edition)
|May 31, 2022
PubMed
Summary

A new database, GpemDB, integrates genomics, phenomics, and enviromics data to accelerate crop breeding. This scalable system manages multi-omics data for efficient analysis and research.

Keywords:
big datacropdatabaseinformative-levelmetadata-levelmulti-omicsphenomicsprecise breedingricevisualization platform

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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.