Integration of variation data through SPARQL Micro-Services Catherine Faron faron@i3s.unice.fr 3 Frederic Metereau frederic.metereau@etu.univ-cotedazur.fr 3 Franck Michel fmichel@i3s.unice.fr 3 Pierre Larmande pierre.larmande@ird.fr 0 1 Guilhem Sempere guilhem.sempere@cirad.fr 1 2 Montpellier France Knowledge Graphs, MongoDB, FAIR data, Genetic variations, Bioinformatics DIADE, IRD, Univ. Montpellier, CIRAD , Montpellier , France French Institute of Bioinformatics (IFB)-South Green Bioinformatics Platform , Bioversity, CIRAD, INRAE, IRD Intertryp , CIRAD, INRAE, IRD, Montpellier , France Université Côte d'Azur , Inria, CNRS, I3S (UMR 7271) , France 2024

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 eficiently 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).

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Sciences problem, as RDF enables data to be interconnected between several databases. This work aims to find a way to interconnect eficiently 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 diferent 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.

[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.