Creating a FAIR-Compliant Metadata Framework for RNA Sequencing: A Case Study on Amyotrophic Lateral Sclerosis

Background: A key challenge in using RNA-seq data is managing the associated metadata, which is often documented manually, inconsistently, and inefficiently, hindering the reproducibility and reusability of RNA-seq research. This is particularly true for complex diseases like Amyotrophic Lateral Sclerosis (ALS), where data is often scattered across multiple platforms and formats. Data and Methods: To address described metadata challenges, we are evaluating existing standards -MIAME/MINSEQE, DCMI/ISA, and EDAM- to identify gaps and guide the development of a unified, FAIR-compliant framework. We are conducting interviews with researchers and data stewards to gather user requirements, focusing on real-world metadata workflows and usability needs. These insights are informing the design of a user-friendly, ontology-driven tool for automated metadata generation. ALS- related RNA-seq datasets serve as a test case to assess the framework's applicability and refine its design. Results: Preliminary findings reveal several critical challenges in current RNA-seq metadata practices: inconsistent terminology across studies and platforms, fragmented and heterogeneous metadata formats, which complicate downstream reusability, and a high dependency on manual input. Researchers and data stewards consistently highlight the need for simplified, standardised and automated metadata entry solutions. The emerging framework will enforce the use of controlled vocabularies and community standards, support both human- and machine-readable formats, and enable interoperability with infrastructures such as WorkflowHub, BioContainers, and bio.tools, adhering to FAIR4RS principles. Conclusions: Our work confirms the urgent need for a standardised, FAIR-compliant approach to RNA-seq metadata management to enhance the reproducibility and impact of research, especially in complex disease contexts like ALS.