=Paper=
{{Paper
|id=Vol-3890/paper-18
|storemode=property
|title=Application of the RDF framework to integrate heterogenous experimental data of a
large chemo- and biodiverse collection from a collaborative research project
|pdfUrl=https://ceur-ws.org/Vol-3890/paper-18.pdf
|volume=Vol-3890
}}
==Application of the RDF framework to integrate heterogenous experimental data of a
large chemo- and biodiverse collection from a collaborative research project==
https://ceur-ws.org/Vol-3890/paper-18.pdf
Application of the RDF framework to integrate
heterogenous experimental data of a large chemo- and
biodiverse collection from a collaborative research project
Frederic Burdet1 , Luis-Manuel Quiros-Guerrero2,3 , Olivier Kirchhoffer2,3 , Jahn Nitschke7 ,
Pierre-Marie Allard2,3,5 , Louis-Felix Nothias2,3,4 , Arnaud Gaudry2,3 , Sébastien Moretti1 ,
Robin Engler1 , Emerson Ferreira Queiroz 2,3 , Nabil Hanna7 , Chunyan Wu8 ,
Antonio Grondin6 , Bruno David6 , Thierry Soldati 7 , Christian Wolfrum8 , Erick Carreira9 ,
Jean-Luc Wolfender2,3 , Marco Pagni1 and Florence Mehl1
1
Vital-IT, SIB Swiss Institute of Bioinformatics, 1015 Lausanne, Switzerland
2
Institute of Pharmaceutical Sciences of Western Switzerland, University of Geneva, 1211 Geneva 4, Switzerland
3
School of Pharmaceutical Sciences, University of Geneva, 1211 Geneva 4, Switzerland
4
Université Côte d’Azur, Institut de Chimie de Nice, Campus Valrose, Nice, France
5
Department of Biology, University of Fribourg, 1700 Fribourg, Switzerland
6
Green Mission Pierre Fabre, Institut de Recherche Pierre Fabre, 31562 Toulouse, France
7
Department of Biochemistry, Faculty of Science, University of Geneva, 1211 Geneva, Switzerland
8
Department of Health Sciences and Technology, ETHZ, 8092 Zürich, Switzerland
9
Department of Chemistry and Applied Biosciences, ETHZ, 8093 Zürich, Switzerland
1. Summary of the project
Plants possess an intricate chemo-diversity, serving as a rich reservoir for the discovery of potential
therapeutic agents. In the framework of a Swiss research initiative called "Sinergia", six research
groups from different disciplines collaborate to explore the potential of more than 17,000 unique dried
plant extracts, to uncover novel bioactive molecules. To do so, heterogenous data from the different
research groups was integrated in a Knowledge Graph (KG). This includes (i) highresolution mass
spectrometry data, (ii) taxonomical information, (iii) chemo-informatics results, (iv) bioassay outcomes
for tuberculosis, obesity, anticancer and antiviral models, as well as (v) data from organic synthetic
chemistry.
We wanted to create a comprehensive framework to facilitate the alignment of multimodal data
across chemical structures, biological activities, spectral features, and taxonomy, among others. At its
core, it would include experimental data that is processed at the sample level, harmonized with external
identifiers whenever feasible, semantically enriched and integrated into the KG.
The KG allows to enhance efficient data mining capabilities to address different scientific research
questions related to the specific objectives of each participating group. For example, "Which bioactive
molecules annotated in the extract of ’plant A’ are present in other species of the same genus?", “Which
taxonomic group presents a remarkable concentration of bioactive molecules for a specific assay?", or
"Which non-toxic active extracts (for a specific assay) present a high probability of containing new
molecules?".
The modelling of data from chemical analysis became a subproject in itself: ENPKG [1]. The recently
published paper describing it exemplifies how this KG can be used to answer questions centered around
the chemistry of incompletely described natural products.
The heterogeneity, expansion, and evolution of the data throughout time posed a major challenge for
the project, centered on managing, integrating, modeling, and effectively sharing data generated by the
different groups. Also, compliance with a data management plan and adherence to open-source science
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principles, particularly the FAIR (Findable, Accessible, Interoperable, Reusable) principles, had to be
ensured.
To overcome these challenges and handle this intricate data, a custom tool called KGSteward [2] was
developed. This tool enables content synchronization based on a centrally managed versioncontrolled
configuration file using Git [3]. This strategic approach provides the flexibility required to address the
global project challenges in effectively managing shared data.
For managing the KG, the free version of GraphDB [4] was chosen, to which KGSteward is connected.
KGSteward updates the KG through the Unix command line and relies on a YAML configuration file
shared through Git, enabling users to host their local instances of the triplestore. The primary functions
performed by KGSteward include: (i) creating the repository, (ii) uploading the TTL files listed in the
configuration file, and (iii) executing UPDATE queries to clean and harmonize the KG.
Table 1 provides a snapshot of the different datasets integrated into the KG, emphasizing their origin.
These datasets, presented as RDF graphs, are organized around the central concept of an "analysis run".
This documents data executed by the same operator within the same laboratory on a specific date.
Should the operator repeat the same assay on another date, it is regarded as a separate analysis run to
ensure comprehensive traceability of integrated data.
Provenance information is encoded using the PROV Ontology [5], linking to raw data files, operators,
protocols, and associated articles. This ensures the ‘Findable and Reusable’ part of the FAIRification.
Additionally, for Interoperability, vocabularies like RDF, RDFS, OWL, are employed to describe data and
metadata extensively, as described in [6]. We also created a customized vocabulary for project concepts
that couldn’t be mapped on existing vocabularies. It is planned to progressively release the data in the
public domain at the same time as the scientific results’ publications. Currently the KG contains about
200 million triples.
The use of RDF/SPARQL in our project has proven to be highly robust, particularly in handling the
dynamic nature of our evolving datasets. These semantic technologies offer a notable level of flexibility
in defining and adapting vocabularies according to our project’s specific requirements.
However, the delicate balance between flexibility and usability was a central concern during the
deployment of our KG. The KG structure was modelled from a starting set of data, but due to the quick
evolution of the cumulative heterogenous sets it was modified drastically for a better overall fit. The
implementation of KGSteward in the evolution process was instrumental, as it bypasses the graphical
interface, automatically detects the modified input TTL files, and does selective updates.
This strategic approach reflects the project’s dedication to effectively capture, organize, and adapt to
the continuously generated data and concepts. The integration of RDF/SPARQL aligns with our project
goals and reflects our commitment to navigate the complexities of contemporary data management
challenges in interdisciplinary research endeavors.
Table 1
List of datasets integrated in the knowledge graph and their origin (non-exhaustive, as the project is still ongoing)
Dataset Analysis description RDF data workflow
CW - bioassays Cytotoxicity test and lipid droplets Experimental results were imported
analyses (ETHZ) with R from multiple excel files, quality
controlled and converted to TTL.
TPH - bioassays Cytotoxicity and bioactivity tests on Same as above.
parasites (SwissTPH)
TS - bioassays Anti-mycobacteria growth inhibition tests Same as above.
(UniGE)
Inventa – in-silico "Novelty score” from untargeted mass In-silico results reformatted with R.
analysis spectrometry data, spectral annotation,
and literature reports (UniGE) [7]
MZmine LC-MS2 Aligned LC-MS data from 1600 plants Experimental results processed at
data – in-silico extracts [8] MassIVE, reimported and converted to
analysis TTL with a Perl script.
LOTUS – Public One of the biggest and best annotated Freeze of Wikidata available from
database resources for natural products occurrences Zenodo; public. SPARQL update was
available free of charge and without any used to reshape and insert the data into
restriction [9] the KG.
TAXO – Public A simplified and balanced taxonomy of Data recompiled from Open Tree of Life
database plants taxonomy and Wikidata with Perl and
SPARQL updates.
ENPKG – Public Experimental Natural Products Knowledge RDF imported directly from Zenodo.
database Graph of the 1600 plant extracts [1]
2. Acknowledgements
The authors are grateful to Green Mission Pierre Fabre, Pierre Fabre Research Institute, Toulouse, France,
for establishing and sharing this unique library of extracts. The authors thank the Swiss National
Science Foundation for received support for the project (SNF N° CRSII5_189921/1).
3. References
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