=Paper=
{{Paper
|id=Vol-3890/paper-35
|storemode=property
|title=Integration of variation data through SPARQL Micro-Services
|pdfUrl=https://ceur-ws.org/Vol-3890/paper-35.pdf
|volume=Vol-3890
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==Integration of variation data through SPARQL Micro-Services==
https://ceur-ws.org/Vol-3890/paper-35.pdf
Integration of variation data through SPARQL
Micro-Services
Frederic Metereau1 , Franck Michel1 , Pierre Larmande2,3,∗ , Guilhem Sempere3,4 and
Catherine Faron1
1
Université Côte d’Azur, Inria, CNRS, I3S (UMR 7271), France
2
DIADE, IRD, Univ. Montpellier, CIRAD, Montpellier, France
3
French Institute of Bioinformatics (IFB)—South Green Bioinformatics Platform, Bioversity, CIRAD, INRAE, IRD,
Montpellier, France
4
Intertryp, CIRAD, INRAE, IRD, Montpellier, France
Abstract
Integrating genetic variations data is essential to understand the interactions involving multiple genes in
complex diseases. However, managing and extracting meaningful information from a large volume of
genotyping data is challenging. This work aims to interconnect efficiently a MongoDB database with
an RDF database through SPARQL Micro-Services. We first developed an RDF Model reusing existing
ontologies and implemented it. Then, we evaluated some examples of queries interconnecting two
applications Gigwa (MongoDB) and AgroLD (SPARQL endpoint).
Keywords
Knowledge Graphs, MongoDB, FAIR data, Genetic variations, Bioinformatics
Genetic variation refers to discrepancies in the DNA sequence among individuals. This
variability in the genome accounts for distinctions in traits like eye colour and blood group,
as well as a person’s susceptibility to certain diseases. While specific traits and diseases can
be attributed to variants in single genes, common conditions such as diabetes, heart disease,
various cancers, Alzheimer’s disease, and Parkinson’s disease to name a few, result from
intricate interactions involving multiple genes and environmental factors. Over 80 million
variant sites in the human genome have been identified, encompassing single nucleotide
polymorphisms (SNPs), insertions and deletions (indels), and other structural variants. The
processing of genetic variation data can reach several Gigabytes to several Terabytes. Indeed,
each genome of individuals is stored and compared to the reference genome of the species.
Thus, the analysis and exploration of this data is a real challenge. A solution to this problem is
to use NoSQL databases tailored to manage large volumes of data with low latency. However,
they lack semantics when the data must be extracted and compared with other data types
such as phenotypes, diseases or gene function. The Semantic Web provides an answer to this
SWAT4HCLS 2024: The 15th International Conference on Semantic Web Applications and Tools for Health Care and Life
Sciences
∗
Corresponding author.
Envelope-Open frederic.metereau@etu.univ-cotedazur.fr (F. Metereau); fmichel@i3s.unice.fr (F. Michel); pierre.larmande@ird.fr
(P. Larmande); guilhem.sempere@cirad.fr (G. Sempere); faron@i3s.unice.fr (C. Faron)
Orcid 0000-0001-9064-0463 (F. Michel); 0000-0002-2923-9790 (P. Larmande); /0000-0001-7429-2091 (G. Sempere);
0000-0001-5959-5561 (C. Faron)
© 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
http://ceur-ws.org
ISSN 1613-0073
CEUR Workshop Proceedings (CEUR-WS.org)
problem, as RDF enables data to be interconnected between several databases. This work aims
to find a way to interconnect efficiently a MongoDB database with another RDF database.
As a proof of concept, we decided to use the Gigwa [1] and AgroLD [2] database applications
to demonstrate the benefits of leveraging data semantics on a high volume of genomic data.
Gigwa is a web application designed to store large volumes of genotypes (up to tens of billions),
initially imported from VCF or other file formats, in a MongoDB [3] database, and to provide a
straightforward interface for filtering these data. It makes it possible to navigate within search
results, visualize them in different ways, and re-export subsets of data into various common
formats. AgroLD is a knowledge graph that exploits Semantic Web technologies to integrate
data of interest for the plant science community. AgroLD is built incrementally spanning
vast aspects of plant molecular interactions. The current phase covers information on genes,
proteins, predictions of homologous genes, metabolic pathways, plant trait associations and
genetic studies.
For this work, we first developed an RDF model based on existing ontologies and inspired by
DisGeNET [4]. We extended it with some features needed for the Gigwa data model which
integrates gene annotation information. Then we developed some SPARQL Micro-Services [5]
using the Gigwa RESTFul API. Finally, we developed and evaluated some queries interconnecting
Gigwa and AgroLD through SPARQL query examples.
References
[1] G. Sempéré, A. Pétel, M. Rouard, J. Frouin, Y. Hueber, F. De Bellis, P. Larmande, Gigwa
v2—Extended and improved genotype investigator, GigaScience 8 (2019). doi:10.1093/
gigascience/giz051 .
[2] A. Venkatesan, G. T. Ngompe, N. E. Hassouni, I. Chentli, V. Guignon, C. Jonquet, M. Ruiz,
P. Larmande, Agronomic Linked Data (AgroLD): A knowledge-based system to enable
integrative biology in agronomy, PLOS ONE 13 (2018) e0198270. doi:10.1371/journal.
pone.0198270 .
[3] A. Kamsky, Adapting TPC-C benchmark to measure performance of multi-document
transactions in MongoDB, Proc. VLDB Endow. 12 (2019) 2254–2262. doi:10.14778/3352063.
3352140 .
[4] J. Piñero, N. Queralt-Rosinach, A. Bravo, J. Deu-Pons, A. Bauer-Mehren, M. Baron, F. Sanz,
L. I. Furlong, DisGeNET: A discovery platform for the dynamical exploration of human
diseases and their genes, Database 2015 (2015). doi:10.1093/database/bav028 .
[5] F. Michel, C. Faron, O. Gargominy, F. Gandon, Integration of Web APIs and Linked Data
Using SPARQL Micro-Services—Application to Biodiversity Use Cases, Information 9 (2018)
310. doi:10.3390/info9120310 .