Federated sharing and processing of genomic datasets for tertiary data analysis.

Area of Science:

• Genomics
• Bioinformatics
• Data Management

Background:

• Next-generation sequencing (NGS) generates vast amounts of data, necessitating robust sharing and processing solutions for biological and clinical applications.
• Current data management practices often centralize processing at sequencing stations, limiting broader access to raw and processed genomic data.

Purpose of the Study:

• To present an innovative extension of the GMQL system architecture to support federated computing for genomic data.
• To enable secure and efficient sharing and querying of distributed genomic datasets.

Main Methods:

• Architectural extensions to the GMQL system for federated data sharing and query processing.
• Development of a system where data is federated via simple sharing instructions.
• Queries are distributed to execution nodes and translated into an intermediate representation for computation.

Main Results:

• A well-designed federated system supporting centralized, distributed, and externalized application styles.
• Enables federated data sharing and query processing through a unified architecture.
• Queries drive data and processing distribution across nodes.

Conclusions:

• The developed system provides a flexible and scalable solution for managing and analyzing distributed genomic data.
• Facilitates collaborative research by enabling seamless access to shared genomic resources.
• The federated approach enhances the utility of NGS data in research and healthcare settings.