Automating biological samples tracking in a multi-omics laboratory

This thesis presents the design and implementation of an automated data management system developed for the Laboratory of Multiomics – Area Sud (LAAS), aimed at improving the handling of high-throughput biological data from sample registration to molecular analysis after sequencing. Grounded in the FAIR principles (Findable, Accessible, Interoperable, and Reusable), the proposed system addresses critical challenges in scientific data management, including data standardization, traceability, security, and reproducibility. The infrastructure integrates laboratory instruments with software platforms, Decos and elabFTW, via custom-built APIs, enabling seamless data exchange, real-time processing, and structured long-term storage. Each step of the experimental pipeline, from biobanking to sequencing, is tracked and recorded through secure, version-controlled workflows, which ensure data integrity and regulatory compliance (e.g., GDPR, HIPAA). The system automates error handling, metadata enrichment, and quality control, while supporting interoperability across diverse data formats and instruments. Case studies involving genomic and transcriptomic pipelines demonstrate the platform’s ability to streamline research workflows, reduce human error, and enhance reproducibility. The work confirms that modular, APIbased architecture offers a scalable solution for managing complex scientific data, and contributes to the broader effort of promoting open, reproducible, and collaborative research in life sciences.