GigaScience · 2026
METRIN-KG: A knowledge graph integrating plant metabolites, traits, and biotic interactions.
СтатьяПоддержка исследования: вне правительства СШАНазвание и аннотация: Перевод в очереди
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Tandon Disha, De Farias Tarcisio Mendes, Allard Pierre-Marie, Defossez Emmanuel
Аннотация
BACKGROUND: In recent years, biodiversity data management has emerged as a critical pillar in global conservation efforts. Today, the ability to efficiently collect, structure, and analyze biodiversity data is central to breakthroughs in conservation, drug development, disease monitoring, ecological forecasting, and agri-tech innovation. However, due to the vastness and heterogeneity of biodiversity data, it is often confined to databases for specific research areas in isolated formats and disconnected from other relevant resources. Crucial components of such data in the kingdom Plantae comprise metabolomes, with the vast array of compounds produced by plants; traits, which are measurable characteristics of plants that influence their growth, survival, and reproduction and affect ecosystem processes; and biotic interactions, including relationships of plants with other living organisms, affecting the ecosystem functions.
RESULTS: In this work, we present METRIN-KG (MEtabolomes, TRaits, and INteractions-Knowledge Graph), a powerful data resource simplifying the integration of diverse and heterogeneous data resources such as plant metabolomes, traits, and biotic interactions.
CONCLUSIONS: The proposed knowledge graph provides an interface to interactively search for data relating to plant metabolomes, traits, and interactions. This, in turn, will facilitate the development of research questions in life sciences. In this context, we provide representative case studies on how to frame queries that can be used to search for relevant data in the knowledge graph.
RESULTS: In this work, we present METRIN-KG (MEtabolomes, TRaits, and INteractions-Knowledge Graph), a powerful data resource simplifying the integration of diverse and heterogeneous data resources such as plant metabolomes, traits, and biotic interactions.
CONCLUSIONS: The proposed knowledge graph provides an interface to interactively search for data relating to plant metabolomes, traits, and interactions. This, in turn, will facilitate the development of research questions in life sciences. In this context, we provide representative case studies on how to frame queries that can be used to search for relevant data in the knowledge graph.