This document summarizes a project to convert biomedical databases like Reactome, CHEBI, UniProt and GO into JSON-LD format and load them into Elasticsearch for full-text search and exploration in Siren Investigate. Key databases were extracted via APIs, converted to JSON-LD using Elasticsearch pipelines, and loaded into Elasticsearch. Visualizations and a relational data model were then created in Siren Investigate to allow faceted browsing and exploration of relationships between datasets. The project demonstrated an effective method for integrating and exploring life science knowledge graphs. Future work includes the Kibio.science project to apply these techniques on their own infrastructure.

1. Reactome2JSON-LD
Converting REACTOME database in JSON-LD
and explore the potential in ElasticSearch/Siren Investigate
François Belleau@ Arnaud Droit Lab
19 January 2021

2. Initial goal

3. Create a Biomart like user interface built around Reactome,
CHEBI, Uniprot and GO databases all interlinked with URIs.
for

4. Project’s final architecture
REST/JSON
REST/JSON
EBI:REST/JSON
WSDL/XML
TXT FILE
XML FILE
1 500 000 documents
from 6 data sources

5. Initial project’s data model

6. Siren Investigate final data model

7. How we did it ?

8. For datasource in [reactome, chebi, uniprot, go, resid, intact]:
1. ETL for data transformation
a. Get the list of object IDs
b. For each document using Python
i. Use API (REST, WSDL, file) to fetch each object
ii. Load document to ElasticSearch index
c. Convert each document to JSON-LD format using Elasticsearch pipeline
d. Export index to Siren Investigate server using elasticsearch-dump
2. With Siren Investigate
a. For each datasource
i. Create visualisation
ii. Combine them to create a dashboard
b. Configure a relational model
c. Explore Reactome data into graph visualizer

9. Simple python scripts to fetch API and load into ES
available here https://github.com/fbelleau/swat4ls-2021/tree/main/stuff2json-ld

10. ES pipelines to transform JSON to JSON-LD
at warp speed in the 4 nodes ES cluster

11. JSON-LD validation @ https://json-ld.org/playground/

12. Build Siren visualization and dashboard
Using a web user interface :
1. Create visualization
2. Edit visualization parameters
3. Add visualisation to dashboard

13. Create relational data model in Siren

14. The BioMart like user
interface and plus...

15. Using free Siren Investigate Community edition
https://siren.io/siren-platform-editions/

16. Slicing and dicing a dataset with facets and text search
Main feature of BioMart

17. Full text search over 6 data sources

18. Full text search in Reactome using relations
Biomart was relational
148
pathways
222
physical entities

19. Visual dashboard with facet browsing

20. Siren Investigate graph browser
ChEBI molecules involved in process GO:0006096 according to Reactome

21. Conclusions

22. Lessons learned
● Crawling existing REST/JSON API is a simple and fast way to access Life
Science data sources
● Converting JSON to JSON-LD is simple and efficient
● Elasticsearch is a good container to store JSON-LD
○ with dereferencable URI for free
○ fast text search and query language
○ efficient disk storage
● Siren Investigate community edition works
○ to browse data with facets (Biomart way)
○ to build visual dashboard
○ to query a relational data model composed of ES indexes
○ to explore the knowledge graph using a performing graph browser
● We now know how to store RDF in Elasticsearch.

23. Next

24. We are building Kibio.science project at ADLab, it is
a new approach of data and knowledge
management that will provide a powerful solution
by integrating and connecting healthcare and
research.
It is builded with Elasticsearch and Siren
Investigate and will be hosted on our own
infrastructure.
We are open for collaboration with the SWAT4LS
community.

25. Arnaud Droit Lab, the
computational biology platform
of the research center of
Quebec
https://compbio.ca/
Thanks to my colleagues at ADLab
● Dr Arnaud Droit (PI)
● Mickael Leclercq
● Regis Ongaro-Carcy
and support from the Siren team
● Manu Agarwal
● Andrew Winter