We are establishing a central data warehouse platform to capture flow cytometric, computational, bioinformatic, and imaging data from tissue biopsies and biological samples. This data is linked to clinical outcome measures and made accessible within the project and, ultimately, to the wider research community.
All validated data from this project is suitable for sharing, ensuring that TRICIA data can be reused for future research. To maximize its impact, we are depositing high-quality data in well-regarded public databases such as the Human Cell Atlas, making it searchable and accessible to researchers beyond the project’s completion.
To ensure consistency and quality, we are implementing a data definition framework for capturing and quality-controlling the data. This enables comparisons with other immune-mediated inflammatory diseases (IMIDs) and establishes a standard for future clinical translation. Our approach aligns with and integrates into the UKRI-funded UK-IMID Bio and CLUSTER consortia. Where possible, we adopt pre-existing data definition standards, such as the IMID-BIO-UK definitions currently being developed, to ensure high-quality metadata capture. The agreed TRICIA standards ensure that data generated within this project is consistently imported into the data warehouse and remains reusable for future research.
We are implementing the tranSMART platform as the core database for TRICIA, capturing sample data and linked clinical information. TranSMART provides a structured database that integrates molecular, phenotypic, and clinical data within an intuitive web-based interface. This platform supports exploratory analyses and dimensional reduction across datasets, facilitating the identification of novel biomarkers and enabling hypothesis-driven research.
To enhance data mining capabilities across IMID datasets, we are integrating emerging tools such as Fractalis into tranSMART. This will create a centralised, well-defined resource for meta-analysis across diseases and datasets. Researchers will be able to access the data through web interfaces and tranSMART APIs, allowing direct integration into computational workflows. These capabilities support meta-analyses across data types and formats, fostering novel hypotheses and applying advanced mathematical modeling, multi-scale computational approaches, and statistical analyses to uncover mechanistic insights into juvenile idiopathic arthritis (JIA).
