FAIRyMAGs: A Modular, FAIR-Compliant Galaxy Workflow Suite for Flexible and Scalable Metagenome-Assembled Genome Reconstruction

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Abstract for both poster and talk Advances in whole-genome sequencing (WGS) have enabled large-scale recovery of metagenome-assembled genomes (MAGs), offering unprecedented insights into microbial diversity across environments. Despite these advances, MAG reconstruction remains computationally intensive and methodologically complex, often requiring the integration of multiple specialized tools for quality control, assembly, binning, refinement, and annotation. Existing workflows frequently rely on scripting-based implementations, limiting accessibility, reproducibility, and adaptability for many researchers and community-driven initiatives. To address these challenges, we present FAIRyMAGs, a FAIR-compliant, modular workflow suite built entirely within the Galaxy platform for generating and analyzing MAGs. FAIRyMAGs is supported by the ELIXIR Biodiversity, food security and pathogens (BFSP) programme and developed in collaboration with four ELIXIR nodes. FAIRyMAGs encompasses six interconnected workflows covering all key steps of MAG reconstruction, including read preprocessing, host contamination removal, assembly, binning, dereplication, and downstream taxonomic and functional annotation. By leveraging Galaxy’s graphical interface, federated compute infrastructure, and robust workflow management, FAIRyMAGs enables researchers to perform complex analyses without requiring local installation or programming expertise. Its modular design allows flexible adaptation, iterative optimization, and seamless integration of new community-contributed tools, empowering both novice and advanced users. In particular, this modular approach, rather than a rigid end-to-end workflow, represents a key advantage of the implementation, and we highlight it here as a model for building more flexible, reusable, and community-extensible Galaxy workflows. Furthermore, integrating a framework into Galaxy to benchmark different binners and bin refiners using the CAMI infrastructure enables the identification of optimal conditions for specific biomes. This approach moves away from the common “more is better” strategy in many pipelines, reducing unnecessary computational effort and its associated environmental impact in high-throughput analyses. We validated FAIRyMAGs using the CAMI II plant-associated benchmark dataset, demonstrating performance comparable or superior to established pipelines such as MGnify, nf-core, or MAGNETO. Additionally, application to four real-world microbiome datasets, spanning host-associated and environmental systems, revealed significant variability in MAG recovery, community complexity, and clustering structure, highlighting the importance of adaptable workflows tailored to dataset-specific characteristics. To further enhance efficiency, FAIRyMAGs incorporates a machine learning-based framework to predict memory requirements for metagenomic assembly, enabling more informed and efficient resource allocation in large-scale analyses. However, current limitations in leveraging workflow outputs for dynamic resource selection within Galaxy’s job scheduling (e.g. TPV-based allocations) prevent this approach from reaching its full potential. This highlights an important opportunity for future development and community discussion within Galaxy.

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