FORUM: building a Knowledge Graph from public databases and scientific literature to extract associations between chemicals and diseases.

Maxime Delmas,Christophe Duperier,Florence Vinson,William Garrier,Olivier Filangi,Yoann Pitarch,Fabien Jourdan,Clément Frainay,Paul-Emeric Saunier,Nils Paulhe,Franck Giacomoni

doi:10.1093/bioinformatics/btab627

Abstract

MotivationMetabolomics studies aim at reporting a metabolic signature (list of metabolites) related to a particular experimental condition. These signatures are instrumental in the identification of biomarkers or classification of individuals, however their biological and physiological interpretation remains a challenge. To support this task, we introduce FORUM: a Knowledge Graph (KG) providing a semantic representation of relations between chemicals and biomedical concepts, built from a federation of life science databases and scientific literature repositories.ResultsThe use of a Semantic Web framework on biological data allows us to apply ontological-based reasoning to infer new relations between entities. We show that these new relations provide different levels of abstraction and could open the path to new hypotheses. We estimate the statistical relevance of each extracted relation, explicit or inferred, using an enrichment analysis, and instantiate them as new knowledge in the KG to support results interpretation/further inquiries.Availability and implementationA web interface to browse and download the extracted relations, as well as a SPARQL endpoint to directly probe the whole FORUM KG, are available at https://forum-webapp.semantic-metabolomics.fr. The code needed to reproduce the triplestore is available at https://github.com/eMetaboHUB/Forum-DiseasesChem.Supplementary information Supplementary data are available at Bioinformatics online.

Highlights

The typical metabolomics data analysis workflow takes as input large collections of spectral data and returns a list of compounds, whose abundances vary significantly between experimental conditions (Giacomoni et al, 2015)
The true-path rule affects corpora sizes of chemical classes and MeSH descriptors by propagating annotated literature to broader concepts, and influences counts used for statistical testing of independence
The Knowledge Graph (KG) of FORUM provides a resource to connect chemical entities to biomedical concepts through millions of scientific articles, fully accessible and searchable, offering a useful tool to support the interpretation of results in metabolomics and yield new hypotheses

Summary

Introduction

The typical metabolomics data analysis workflow takes as input large collections of spectral data and returns a list of compounds, whose abundances vary significantly between experimental conditions (Giacomoni et al, 2015). The profiling of small molecules in a tissue, a fluid or an organism using high throughput untargeted analysis is a powerful tool for the identification of biomarkers (Ludwig and Hummon, 2017). It offers valuable insights for diagnosis and patient prognosis, and opens promising opportunities for drug design and physiopathology understanding.

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