A metadata managed FAIR end-to-end workflow for microbial community Omics data analysis

BackgroundMolecular profiling using high-throughput omics technologies has tremendously increased our ability to interrogate complex microbial communities at the molecular level. In the context of data reuse, the FAIRification of these extensive datasets is frequently perceived as a secondary administrative task, addressed only after data analysis has been completed. However, this approach overlooks the potential benefits of early metadata integration as the procedures for processing and analyzing raw data are primarily dictated by the underlying research design and experimental conditions. Gathering interoperable research metadata at the earliest stages creates a standardized basis for managing, processing, and analyzing data enabling more efficient and reproducible FAIR workflows. ResultsThe single containment principle was used to develop modular containerized reproducible workflows that support the FAIR principles for research software by systematically capturing standardized metadata for each data processing step along with the resulting data products. Using defined mock metagenomic datasets as an example, we show that interoperable research metadata can be used to drive such computational workflows. By processing raw data accordingly, machine-actionable provenance chains are created that enhance the reproducibility and reusability of the resulting data products. ConclusionsA seamless integration of wet lab experiments with computational investigations is essential for a FAIR end-to-end research process. Meta-data-managed workflows prevent the need for unnecessary data manipulation. Workflow provenance registration explicates the complex multi-step methods employed for data processing and analysis. Combining FAIR principles with data provenance registration enhances the reusability of omics datasets by promoting transparency and reproducibility. O_TEXTBOXKey PointsO_LIWe present the first metadata-managed FAIR end-to-end workflow for microbial community Omics data analysis. C_LIO_LIThe framework links experiment metadata with CWL workflows, enabling seamless metadata-driven execution from experiment to data product. C_LIO_LIComprehensive provenance tracking using RDF standards creates machine-actionable chains connecting wet-lab protocols to computational outputs, enhancing reproducibility and supporting automated workflow validation. C_LIO_LIBenchmarking with defined mock communities demonstrates workflow reliability across assembly methods while generating FAIR-compliant research objects suitable for community reuse and comparative studies. C_LI C_TEXTBOX