Towards FAIRification of sensitive and fragmented rare disease patient data: challenges and solutions in European reference network registries

Abstract

Introduction Rare disease patient data are typically sensitive, present in multiple registries controlled by different custodians, and non-interoperable. Making these data Findable, Accessible, Interoperable, and Reusable (FAIR) for humans and machines at source enables federated discovery and analysis across data custodians. This facilitates accurate diagnosis, optimal clinical management, and personalised treatments. In Europe, twenty-four European Reference Networks (ERNs) work on rare disease registries in different clinical domains. The process and the implementation choices for making data FAIR (‘FAIRification’) differ among ERN registries. For example, registries use different software systems and are subject to different legal regulations. To support the ERNs in making informed decisions and to harmonise FAIRification, the FAIRification steward team was established to work as liaisons between ERNs and researchers from the European Joint Programme on Rare Diseases. Results The FAIRification steward team inventoried the FAIRification challenges of the ERN registries and proposed solutions collectively with involved stakeholders to address them. Ninety-eight FAIRification challenges from 24 ERNs’ registries were collected and categorised into “training” (31), “community” (9), “modelling” (12), “implementation” (26), and “legal” (20). After curating and aggregating highly similar challenges, 41 unique FAIRification challenges remained. The two categories with the most challenges were “training” (15) and “implementation” (9), followed by “community” (7), and then “modelling” (5) and “legal” (5). To address all challenges, eleven types of solutions were proposed. Among them, the provision of guidelines and the organisation of training activities resolved the “training” challenges, which ranged from less-technical “coffee-rounds” to technical workshops, from informal FAIR Games to formal hackathons. Obtaining implementation support from technical experts was the solution type for tackling the “implementation” challenges. Conclusion This work shows that a dedicated team of FAIR data stewards is an asset for harmonising the various processes of making data FAIR in a large organisation with multiple stakeholders. Additionally, multi-levelled training activities are required to accommodate the diverse needs of the ERNs. Finally, the lessons learned from the experience of the FAIRification steward team described in this paper may help to increase FAIR awareness and provide insights into FAIRification challenges and solutions of rare disease registries.

Landscape analysis of available European data sources amenable for machine learning and recommendations on usability for rare diseases screening

“…ML and AI are increasingly being used in healthcare and genetic research to improve patient outcomes and advance scienti c knowledge. Further, their application can be bene cial for early diagnosis, improving prognosis, and treatment decisions in the eld of RDs [17]. However, such technology relies heavily on the availability of highquality data, which in the RD domain is often scarce and fragmented [18].…”

Assessing Resolvability, Parsability, and Consistency of RDF Resources: A Use Case in Rare Diseases

“…• A list of FAIR implementations. Data stewards in the EJP RD [11,12] use this document to record the implementation status of the various resources related to rare diseases. From these resources, we selected those for which an RDF representation exists.…”

Resources and tools for rare disease variant interpretation

“…A recent initiative, aiming at making FAIR (‘FAIRification’) 24 ERN (European Reference Networks) registries of RD patients, allowed collecting ninety-eight critical FAIRification challenges and proposing solutions to address them ( Dos Santos Vieira et al, 2022 ). Awareness of the FAIRification challenges learned from initiatives like this one, which are strongly supported by the ELIXIR community, plays an important role in identifying solutions aimed at harmonizing RD data.…”