=Paper=
{{Paper
|id=Vol-3415/paper-24
|storemode=property
|title=FAIRification and semantic modelling for Duchenne and Becker Muscular Dystrophy rare
diseases
|pdfUrl=https://ceur-ws.org/Vol-3415/paper-24.pdf
|volume=Vol-3415
|dblpUrl=https://dblp.org/rec/conf/swat4ls/Perdomo-Quinteiro23
}}
==FAIRification and semantic modelling for Duchenne and Becker Muscular Dystrophy rare
diseases==
https://ceur-ws.org/Vol-3415/paper-24.pdf
FAIRification and semantic modelling for Duchenne
and Becker Muscular Dystrophy rare diseases
Pablo Perdomo-Quinteiro1,∗ , Sergiu Siminiuc2 , Paraskevi Sakellariou2 , Marco Roos1 ,
Pietro Spitali1 and Núria Queralt-Rosinach1,∗
1
Leiden University Medical Center, Leiden, the Netherlands
2
Duchenne Data Foundation, Veenendaal, the Netherlands
Abstract
The BIND project is a EU-funded project that attempts to improve the characterisation of brain involve-
ment in Duchenne and Becker Muscular Dystrophies (DMD and BMD). Here, we present our ongoing
work on making multimodal data FAIR, the semantic models, and discuss challenges and opportunities
based on our FAIRification experience.
Keywords
FAIR data, semantic modelling, BYOD, Duchenne Muscular Dystrophy, Becker Muscular Dystrophy,
Rare Diseases
Motivation
The BIND project is a EU-funded project that attempts to improve the characterisation of brain
involvement in Duchenne and Becker Muscular Dystrophies (DMD and BMD). To achieve
this, 19 organizations across Europe and Japan are collaborating and obtaining phenotypic and
molecular data such as transcriptomic, proteomic, clinical, behavioral, and MRI brain images. It
is crucial to make this information Findable, Accessible, Interoperable and Reusable (FAIR), as
making data FAIR will allow machines to exchange, integrate and analyze these data within
the consortium and with other external FAIR data sources. Additionally, making data FAIR
improves transparency, reproducibility, and maximizes the use of scientific output. Ultimately,
FAIR data will advance our understanding of DMD and BMD and drive innovation on new
therapeutic strategies. In order to achieve FAIRification, i.e., the process of making data FAIR,
data must be properly described and annotated using standard metadata models and ontologies
used by the DMD and BMD community for research, and must be stored in a persistent and
SWAT4HCLS 2023: The 14th International Conference on Semantic Web Applications and Tools for Health Care and Life
Sciences
∗
Corresponding author.
Envelope-Open P.Perdomo_Quinteiro@lumc.nl (P. Perdomo-Quinteiro); sergiu@duchennedatafoundation.org (S. Siminiuc);
sakellariou.elvina@duchennedatafoundation.org (P. Sakellariou); M.Roos@lumc.nl (M. Roos); P.Spitali@lumc.nl
(P. Spitali); N.Queralt_Rosinach@lumc.nl (N. Queralt-Rosinach)
Orcid 0000-0001-8784-0907 (P. Perdomo-Quinteiro); 0000-0002-1531-1560 (S. Siminiuc); 0000-0002-9091-0053
(P. Sakellariou); 0000-0002-8691-772X (M. Roos); 0000-0003-2783-688X (P. Spitali); 0000-0003-0169-8159
(N. Queralt-Rosinach)
© 2023 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
CEUR Workshop Proceedings (CEUR-WS.org)
CEUR
http://ceur-ws.org
Workshop ISSN 1613-0073
Proceedings
accessible repository. Additionally, data must be available in machine-readable formats and
should follow best practices for data management and sharing.
The FAIRification process has been developed together with EBRAINS, a digital research
infrastructure created by the EU-funded Human Brain Project with the objective of understand-
ing human brain function and disease. By making use of EBRAINS standards, the project can
benefit from all the information gathered in EBRAINS, and at the same time, other researchers
can make use of the data provided by BIND. The FAIRification of the data has been done using
Semantic Web technologies (RDF, OWL and SHEX) and reusing the semantic models provided
by the European Joint Programme on Rare Diseases (EJP RD). Our approach is to use FAIR
implementations selected by the rare disease community. To this end, we worked in collabo-
ration with the EJP RD, as a key driver project on FAIRification of rare disease resources for
research and a large representation of the rare disease community. Our method is based on the
FAIRification workflow [1], where the first step is to assess the FAIR status of the data.
Several Bring Your Own Data (BYOD) workshops [2] have been organized throughout 2022,
with the two-fold objective of assessing the FAIR status of the data and explaining the meaning
of the data to FAIR experts and bringing the FAIRification methodology closer to the data
owners. In these workshops, we as FAIR experts needed to fully understand the data to correctly
perform the FAIRification process. One of the challenges is that data owners are often unfamiliar
with the FAIRification process, and viceversa, FAIR experts are often not familiar with domain
specific knowledge. Another challenge is to reach a consensus of what the data mean and how
best to represent it. The modelling has been developed following the semantic core data model
of the set of common data elements for rare disease patient registries provided by the EJP RD [3].
Here, we present our ongoing work on making multimodal data FAIR, the semantic models we
are developing to represent data such as patient phenotypic behavioral information compiled in
the form of questionnaires or single cell RNA-seq data obtained in preclinical mouse models;
and discuss challenges and opportunities based on our FAIRification experience so far.
Acknowledgments
We thank all BIND team, the data owners and domain experts. We also thank the EJP RD for
providing the semantic models used in this project. This project was supported by a grant
from the European Union’s Horizon 2020 research and innovation programme under grant
agreement No 847826 (Brain Involvement iN Dystrophinopathies (BIND)).
References
[1] A. Jacobsen, et al., A Generic Workflow for the Data FAIRification Process,
Data Intelligence 2 (2020) 56–65. URL: https://direct.mit.edu/dint/article/2/1-2/56/9988/
A-Generic-Workflow-for-the-Data-FAIRification. doi:1 0 . 1 1 6 2 / D I N T _ A _ 0 0 0 2 8 .
[2] R. Hooft, et al., ELIXIR-EXCELERATE D5.3: Bring Your Own Data (BYOD) (2019). URL:
https://zenodo.org/record/3207809. doi:1 0 . 5 2 8 1 / Z E N O D O . 3 2 0 7 8 0 9.
[3] R. Kaliyaperumal, et al., Semantic modelling of common data elements for rare disease
registries, and a prototype workflow for their deployment over registry data, Journal of
Biomedical Semantics 13 (2022) 1–16. URL: https://jbiomedsem.biomedcentral.com/articles/
10.1186/s13326-022-00264-6. doi:1 0 . 1 1 8 6 / S 1 3 3 2 6 - 0 2 2 - 0 0 2 6 4 - 6 / F I G U R E S / 6 .