Datamanagement in Multiomics Research Projects

While analytical and statistical frameworks for integration have advanced rapidly, the foundational constraints are increasingly infrastructural and organizational rather than purely methodological. Multi-omics integration presents profound data management challenges arising from scale, heterogeneity, semantic inconsistency, dynamic versioning, regulatory constraints, and long-term sustainability requirements.

This paper develops a systems-level analysis of these challenges and argues that robust, FAIR-aligned, provenance-aware, and interoperable data infrastructures are indispensable for credible and reproducible integrative biology. By examining metadata formalization, storage architectures, schema design, workflow versioning, governance frameworks, and lifecycle management, this work situates multi-omics integration within the broader context of data engineering and information systems theory.