NExtSEEK: Extending SEEK for Active Management of Interoperable Metadata

Area of Science:

• Biotechnology
• Bioinformatics
• Data Science

Background:

• Effective data management is crucial for research rigor and reproducibility, especially in large, multi-institutional projects.
• Existing repositories often struggle with the metadata demands of multi-omic data spanning diverse sources.
• The SEEK platform offers metadata flexibility but assumes immutable sample data, limiting its use for ongoing studies.

Purpose of the Study:

• To enhance the SEEK platform for active data management of multi-omic studies.
• To address limitations in handling sequential sample events and diverse metadata in ongoing research.
• To improve data reusability and reproducibility through a more flexible data model.

Main Methods:

• Developed a modified wrapper for the SEEK platform to enable active data management.
• Introduced discrete, mutable sample types to accommodate expanded metadata tracking.
• Restructured assays from nodes to edges, creating a network model of experimental processes.

Main Results:

• The modified SEEK platform allows for dynamic tracking of sequential sample events across multiple sites.
• Researchers can now collect and organize richer metadata for ongoing studies.
• The network model more accurately represents complex experimental workflows.

Conclusions:

• The enhanced SEEK platform facilitates improved data and metadata organization for multi-omic research.
• This approach supports better data reusability and reproducibility in complex, longitudinal studies.
• The modifications enable more effective data sharing through public repositories.