=Paper=
{{Paper
|id=Vol-3415/paper-47
|storemode=property
|title=Data in use for Alzheimer disease study: combining gene expression, orthology, bioresource
and disease datasets
|pdfUrl=https://ceur-ws.org/Vol-3415/paper-47.pdf
|volume=Vol-3415
|dblpUrl=https://dblp.org/rec/conf/swat4ls/FariasKSDCBM23
}}
==Data in use for Alzheimer disease study: combining gene expression, orthology, bioresource
and disease datasets==
https://ceur-ws.org/Vol-3415/paper-47.pdf
Data in use for Alzheimer disease study: combining
gene expression, orthology, bioresource and disease
datasets
Tarcisio Mendes de Farias1,2,∗ , Tatsuya Kushida3,∗ , Ana C. Sima1,2 ,
Christophe Dessimoz1,2 , Hirokazu Chiba4 , Frédéric Bastian1,2,† and Hiroshi Masuya3,†
1
SIB Swiss Institute of Bioinformatics, Lausanne, Switzerland
2
University of Lausanne, Lausanne, Switzerland
3
BioResource Research Center, RIKEN, Tsukuba-shi, Japan
4
Database Center for Life Science, ROIS, Kashiwa-shi, Japan
The BioResource Research Center (BRC) at the Japanese Institute of Physical and Chemical
Research (RIKEN) develops and maintains the RIKEN BioResource MetaDatabase. This database
integrates several life science datasets to support researchers in making a comprehensive use of
RIKEN’s research results. For instance, RIKEN BRC collects, preserves and distributes various
bioresources for further scientific research: experimental mouse strains, cultured cell lines and
genetic materials of human and animal origin. In this article, we present a case study that
mainly combines the RIKEN BioResource MetaDataBase [1] and the Bgee, a multi-species gene
expression database [2]. Our use case involves finding the Alzheimer disease (AD) related human
genes that are highly expressed in the healthy prefrontal cortex, and the RIKEN’s genetically
modified mice related to these genes. Information on the gene expression is obtained from the
Bgee database and the human-mouse orthologs from the Orthologous MAtrix (OMA) database
[3]. Orthologs are pairs of genes which have evolved from a single gene in the last common
ancestor. The DisGeNET dataset provides relationships between disease and human genes [4].
In summary, we combined the four aforementioned data sources by writing the federated
SPARQL query at https://purl.org/data-in-use and illustrated in Fig. 1. This query can be exe-
cuted in the SPARQL endpoint at https://knowledge.brc.riken.jp/sparql. Examples of retrieved
results are shown in Table 1. Among them, the APOE gene is highly relevant for AD research
as shown in [5]. Furthermore, we evaluated two query execution scenarios. One scenario con-
siders a SERVICE SPARQL subquery to be executed against the remote Bgee SPARQL endpoint
resulting in an average runtime of about 60 seconds and, thus, assuring access to the latest data.
The second scenario replaces the SERVICE subquery with a subquery matching triple patterns
from the named graph containing Bgee data and stored in the RIKEN BioResource MetaDatabase.
As a result, the query runtime is drastically reduced from 60 seconds to around 26 seconds
(i.e., 2.3x faster). Therefore, we compared federated versus centralised data access and storage
SWAT4HCLS 2023: The 14th International Conference on Semantic Web Applications and Tools for Health Care and Life
Sciences.
∗
Co-first authors. †Co-last authors.
Envelope-Open tarcisio.mendes@sib.swiss (T. Mendes de Farias)
© 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
http://ceur-ws.org
ISSN 1613-0073
CEUR Workshop Proceedings (CEUR-WS.org)
Table 1
Query result example.
Mouse ID RIKEN’s mouse homepage Ensembl identifier Gene
RBRC06342 http://www2.brc.riken.jp/lab/animal/detail.php?brc_no=RBRC06342 ENSG00000142192 APP
RBRC03391 http://www2.brc.riken.jp/lab/animal/detail.php?brc_no=RBRC03391 ENSG00000130203 APOE
Figure 1: A simplified visualisation of the query graph patterns of our “data in use” example.
approaches for our use case. Although the query execution performance is significantly better
in the centralised setup, unfortunately, it does not retrieve the latest results since it currently
stores an older version of the Bgee database when compared to the data available via the remote
Bgee SPARQL endpoint at https://bgee.org/sparql/. Moreover, the federated setup retrieves 12
more results than the centralised one. To avoid longer runtimes and query timeout, in both
scenarios, we used the locally stored OMA and DisGeNET as named graphs in the RIKEN
MetaDataBase.
Acknowledgments. Funding from State Secretariat for Education, Research and Innovation
(SERI) via ETHZ grant BG 02-072020 and EU Horizon 2020 INODE grant 863410.
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