Multi-omics datasets are an increasingly prevalent and necessary resource for achieving scientific advances in microbial ecosystem research. However, they present twin challenges to research infrastructures: firstly the utility of multi-omics datasets relies entirely on interoperability of omics layers, i.e. on formalised data linking. Secondly, microbiome derived data typically lead to computationally expensive analyses, i.e. on the availability of powerful compute infrastructures.
Historically, these challenges have been met within the context of individual database resources or projects. These confines limit the FAIRness of datasets (since they typically aren’t interlinked, directly comparable, or collectively indexed), and mean the scope to analyse such datasets is governed by the available resources of the given project or service. Removing these confines, by establishing a model for the federated analysis of microbiome derived data, will allow these challenges to be met by the community as a whole. More compute can be brought to bear by combining EOSC and ELIXIR infrastructures, Galaxy instances, and existing resources like EMBL-EBI’s MGnify, but this requires adopting a common schema for sharing analysed datasets, including their provenance.
Such a schema can also directly contribute to the interlinking of omics layers, using research objects to connect linked open datasets. We aim to design and implement a schema for this purpose, and use it to allow the generation of comparable analyses on heterogeneous compute infrastructures. By doing so, it will streamline the deposition of accessioned analysis products into public databases.
Alexander Rogers, Alexander Sczyrba