The ELIXIR Metabolomics Community relies on standards, formats and data treatment solutions development and adoption, but it remains challenging to ensure high-quality reported metadata, sufficiently contextualised results, interoperable and reusable datasets and to integrate these metabolomics data with other omics or studies. 

This project is designed to address these issues and aims to connect key international standards with ELIXIR resources, as well as creating associated community guidelines and training materials. 

Spatial transcriptomics (ST) was named ‘Method of the Year 2020’ by Nature Methods and was more recently featured in Nature’s Seven technologies to watch in 2024. ST is now a prerequisite for researching transcriptional pathology at the cellular and molecular levels. Current use of ST is ubiquitously applied to multiple pathologies, including neurodegenerative disease, cancer, cardiomyopathy and nephrology.

This project aims to strengthen the basis for a one-stop shop connecting databases, datasets and tools for the deployment of the engineering Design-Build-Test-Learn (DBTL) framework in biotechnology. It will do so by surveying the tools and data landscape, pinpointing gaps and opportunities, and establishing design patterns for task-specific workflows for analysis, integration and sharing of multimodal data. 

This project addresses the limitations of current ontologies in capturing the dynamic nature of disordered protein regions by pursuing several primary objectives. Firstly, novel structural and functional ontologies will be developed to accurately represent the structural heterogeneity and dynamic functional annotations of proteins. These ontologies will incorporate timescales, annotating the kinetics of structural transformations to elucidate molecular mechanisms and regulatory pathways governing protein dynamics.