=Paper=
{{Paper
|id=Vol-3890/paper-44
|storemode=property
|title=FAIR Data Cube, a FAIR data infrastructure for integrated multi-omics data analysis
|pdfUrl=https://ceur-ws.org/Vol-3890/paper-44.pdf
|volume=Vol-3890
}}
==FAIR Data Cube, a FAIR data infrastructure for integrated multi-omics data analysis==
https://ceur-ws.org/Vol-3890/paper-44.pdf
FAIR Data Cube, a FAIR data infrastructure for
integrated multi-omics data analysis
Xiaofeng Liao1,∗ , Yuliia Orlova5 , Cenna Doornbos1 , Anna Niehues1,2 , Casper de
Visser1 , Junda Huang1 , Thomas H.A. Ederveen1 , Purva Kulkarni1,2,3 , K. Joeri van der
Velde4 , Morris A. Swertz4 , Martin Brandt5 , Alain J. van Gool2,3 and Peter A.C. ’t
Hoen1,∗
1
Medical BioSciences department, Radboud university medical center, Nijmegen, The Netherlands
2
Translational Metabolic Laboratory, Department of Laboratory Medicine, Radboud university medical center, Nijmegen,
The Netherlands
3
Department of Human Genetics, Radboud university medical center, Nijmegen, The Netherlands
4
Genomics Coordination Center, University of Groningen and University Medical Center Groningen, Groningen, The
Netherlands
5
SURF, Science Park 140, Amsterdam,The Netherlands
Abstract
We are witnessing an enormous growth in the amount of molecular profiling (omics) data enabeling the
integration of multi-omics data. Nonetheless, this is challenging due to the lack of FAIR -omics data and
metadata. The storage of human -omics data in secure silos, for privacy reasons, further complicates
their reuse. Federated analysis of FAIR data is a privacy-preserving solution to make optimal use of these
multi-omics data and transform them into actionable knowledge.
The Netherlands X-omics Initiative is a National Roadmap Large-Scale Research Infrastructure aiming
for efficient integration of data generated within X-omics and external datasets. To facilitate this, we
developed the FDCube, which adopts and applies the FAIR principles and helps researchers to create
FAIR data and metadata, facilitate reuse of their data, perform federated analysis, and make their data
analysis workflows transparent.
Keywords
FAIR, Multi-omics, FAIR Data Cube, Metadata, Federated Analysis
1. Introduction
It is now widely acknowledged that in order to truly advance our understanding of health, it is
required to combine -omics data from different sources. Nonetheless, this remains challenging
as data and their associated metadata are not always findable, accessible, interoperable, and
SWAT4HCLS 2024: The 15th International Conference on Semantic Web Applications and Tools for Health Care
and Life Sciences, February 26–29, 2024, Leiden, The Netherlands
∗
Corresponding author.
XiaoFeng.Liao@radboudumc.nl (X. Liao); yuliia.orlova@surf.nl (Y. Orlova);
Cenna.Doornbos@radboudumc.nl (C. Doornbos); annaniehues@eatris.eu (A. Niehues);
Casper.deVisser@radboudumc.nl (C. de Visser);
Junda.Huang@radboudumc.nl (J. Huang); Tom.Ederveen@radboudumc.nl (T. H.A. Ederveen);
Purva.Kulkarni@radboudumc.nl (P. Kulkarni); joeriv@gmail.com (K. J. van der Velde); m.a.swertz@gmail.com
(M. A. Swertz); martin.brandt@surf.nl (M. Brandt); Alain.vanGool@radboudumc.nl (A. J. v. Gool);
Peter-Bram.tHoen@radboudumc.nl (Peter A.C. ’t Hoen)
0000-0002-4706-1084 (X. Liao)
© 2024 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
ceur-ws.org
Workshop ISSN 1613-0073
Proceedings
reusable (FAIR). Furthermore, as most -omics data are derived from a human source, these data
are mainly stored in secure and protected data silos. It remains a challenge to re-use these
highly secured data sets without the risk of infringing the privacy of the individuals from which
the data are derived. Hence, there is a need for tools that enable federated data analysis.
Here we present the X-omics FAIR Data Cube (FDCube). The FDCube helps to make -omics
data comply with the FAIR principles and provides a federated data analysis mechanism to
bring algorithms to data stations, in order to facilitate data reusing and analysis, while ensuring
data privacy.
2. Result
The architecture of FDCube is presented in Figure 1A. The FDCube infrastructure allows
dataset owners to register data on the FAIR Data Point (FDP) and it incorporates the FAIR Data
Station, a metadata capture platform that facilitates making data FAIR at the source. Using
the Investigation-Study-Assay (ISA) metadata schema, metadata is transformed into a FAIR
machine-actionable resource stored in an RDF triplestore.
Researchers can exploit the FDCube to find datasets and initiate computation requests to
dataset owners. These federated analysis requests are executed on the respective datasets
through Vantage6, and the results are communicated back.
We adopted the Trusted World of Corona (TWOC)1 project as a demonstration to show how
to utilize the FDCube for integrated multi-omics federated analysis. TWOC is developing an
information platform containing scientific data & information and real world clinical observa-
tions on Corona. Figure 1B illustrates the use of FDCube on the TWOC dataset by showing the
pipelines covering multiple functionalities.
The FDCube is now listed as a catalog item in the SURF Research Cloud, in which an in-a-box
solution is provided to deploy the collection of software applications used by FDCube, including
the FAIR Data Point, GraphDB, and FAIR Data Station.
Figure 1: High level architecture of FDCube and its demonstration on TWOC project
1
https://www.health-holland.com/project/2020/trusted-world-of-corona