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PlantMDCS: A code-free, modular toolkit for rapid deployment of plant multi-omics databases

Chen, C.; Liu, Y.; Wang, L.; Sai, J.; Wang, Y.; Yue, W.; Sun, J.; Li, Z.; Wang, F.; Tian, J.; Xu, D.; Fang, Y.

2026-02-11 bioinformatics
10.64898/2026.02.09.704752 bioRxiv
Show abstract

With the rapid accumulation of diverse omics datasets, achieving efficient management and integrative analysis of plant multi-omics data remains a major challenge. Conventional solutions rely on constructing web-based databases, which often demand substantial programming expertise and long-term financial support. To address these limitations, we developed the Plant Multi-omics Database Construction System (PlantMDCS)-a locally deployable, user-friendly, and collaborative platform that unifies database construction and downstream multi-omics analysis within a graphical environment. PlantMDCS adopts a decoupled front-end/back-end architecture. The back end serves as the core engine for data management and computation, and is responsible for the storage, preprocessing, integration, and hierarchical association of multi-omics data. Once initialized, the front end supports the complete research workflow, including data import, querying, integrative analysis and visualization. All operations can be performed without programming, while local resource usage is dominated by disk storage required for user-provided datasets rather than sustained computational overhead. Benchmarking across plant species ranging from Arabidopsis to hexaploid wheat demonstrated that database construction can be completed within minutes, independent of genome size or data complexity. PlantMDCS is designed for local deployment to ensure data security, while allowing multi-user collaboration within local networks and supporting controlled remote access for teams distributed across different regions. Overall, PlantMDCS offers a secure and sustainable framework that integrates data management and analysis within a unified system. This design shifts multi-omics research away from fragmented file-based processing toward persistent, database-driven exploration, thereby enhancing analytical efficiency and reproducibility.

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