METRIN-KG: A knowledge graph integrating plant metabolites, traits, and biotic interactions

  1. Disha Tandon
  2. Tarcisio Mendes De Farias
  3. Pierre-Marie Allard
  4. Emmanuel Defossez

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Abstract

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.

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  1. GigaScience

    AbstractBackground 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 kingdom Plantae comprise of metabolomes - the vast array of compounds produced by plants; traits - measurable characteristics of plants that influence their growth, survival, and reproduction, and that affect ecosystem processes; and biotic interactions - 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 plant metabolomes, traits, and interactions. This, in turn, will facilitate 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.

    This work has been peer reviewed in GigaScience (see https://doi.org/10.1093/gigascience/giag051), which carries out open, named peer-review. These reviews are published under a CC-BY 4.0 license and were as follows:

    Reviewer 2：

    The authors present METRIN-KG, a knowledge graph integrating plant metabolomic, trait, and biotic interaction datasets. The work should have substantial value for multiple plant sciences and ecology domains. The overall effort to harmonize disparate resources into an integrated, semantically coherent resource is impressive. The methodology includes a notable pipeline for ontology alignment across multiple sources. However, despite its technical strengths, several issues regarding data accessibility and manuscript structure and clarity should be addressed. The manuscript is highly technical throughout. While this level of precision is exemplary, it may alienate biologically oriented readers, and effort should be made so that the impact and manuscript is clear to a larger audience.

    Major comments

    1. The online deployment currently presents several problems that must be resolved before publication.
    1. Usability represents a significant barrier. Many potential users such as biologists without semantic-web or RDF experience are unlikely to be able to interpret the current figures or formulate SPARQL queries. The manuscript briefly mentions ExpasyGPT, which has strong potential to overcome this barrier by allowing natural language querying. This tool should be emphasized more prominently, potentially with a dedicated subsection and discussion of its role in broadening the resource's accessibility.
    1. To fully demonstrate the relevance of METRIN-KG, the use-case section would benefit from quantitative summaries, visualizations (e.g., distributions, network visualizations), and biological interpretations of query results.
    1. The overall manuscript organisation would benefit from restructuring to improve readability and better expose the impact of the work done.

    • The methodological content currently in "Mapping of TRY data" and "Mapping of GloBI data" should be moved to the Methods section, while the quantitative outputs (e.g., numbers of records) should be moved into a dedicated Results section.

    • The current "Data re-use and case studies" section could be reorganised into Results and Discussion sections,

    • Results include: description of outputs from the methodological steps (e.g. the ontology, the successfulness of the mapping process, the size of the final knowledge graph/number of triples, and other relevant metrics; the user interface, including being able to share and add example questions and write NL questions via ExpasyGPT; examples of SPARQL queries and case studies.

    • Discussion includes: Reuse potential; case-study interpretations or impact; future directions including planned expansion and enhancing of ontological structure, etc.

    1. An overview and evaluation of the KG should be provided, for example the number of plant species, and the distributions of connections. Any gaps or any (potentially) biases due to the input data needs to be acknowledged. For example, it is very likely certain species may be under/overrepresented in either metabolome or interaction datasets, or possibly geographic skews could exist.

    1. The ontology is variously referred to as the "Earth Metabolome Ontology", "EMI ontology", and "EMI". Consistent naming should be adopted throughout the manuscript and associated repositories. It is also unclear whether the ontology is a result of this work. As written, the "Ontology" section under "Methods" reads more like a result/description than a methodological step. Clarifying what components are original contributions and presenting them in Results would strengthen the manuscript. Additionally, the phrases "our proposed framework" and "our approach" are ambiguous, do these refer to the ontology itself, the metadata-mapping pipeline, or the overall integration process? Finally, referring to METRIN-KG as a "tutorial to build a knowledge graph" appears to be a bit out of place, given the topic of the manuscript.

    2. Given its potential relevance beyond this project, the authors are strongly encouraged to publish the code for the metadata-mapping pipeline. In addition, the following details would strengthen the methodological rigor:

    • Were any acceptability criteria implemented in the automated step, e.g. a minimum Cosine similarity threshold?

    • Were the manual corrections systematically documented?

    • In how many cases were manual corrections needed?

    • Was any evaluation done on the embedding/model version or the source of errors?

    Minor comments

    1. The manuscript would benefit from proofreading for consistent use of Oxford commas (and an "&" instead of "and").

    2. Reference 190 contains a typo "GiHub"

    3. References should be checked (e.g. citation for [95] references both METRIN-KG Zenodo and GloBI Zenodo)

    4. The METRIN-KG Zenodo link in the article is not to the latest version (Version 5)

    5. Consider improving the figures, e.g. use of colour to better communicate the content and refining layouts.

    6. The authors should deposit a snapshot of the GitHub repository to Zenodo.

    The following are suggestions to the authors, to be followed by their own judgement:

    1. Table 1, Figure 3, Figure 4 could be moved to supplementary material to make space for figures for the case studies.

    2. The dense in-text list of ontologies in the "Metadata mapping" section could be replaced with a summarized table (e.g. by moving Supplementary Table 2, but including references to the main text).

    3. The full SPARQL queries in "Taxonomy mapping" could be moved to supplementary materials, with a high-level description left in the main text.

  2. GigaScience

    AbstractBackground 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 kingdom Plantae comprise of metabolomes - the vast array of compounds produced by plants; traits - measurable characteristics of plants that influence their growth, survival, and reproduction, and that affect ecosystem processes; and biotic interactions - 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 plant metabolomes, traits, and interactions. This, in turn, will facilitate 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.

    This work has been peer reviewed in GigaScience (see https://doi.org/10.1093/gigascience/giag051), which carries out open, named peer-review. These reviews are published under a CC-BY 4.0 license and were as follows:

    Reviewer 1：

    This study makes an important and timely contribution to plant ecology by combining multiple data sources of plant functional traits, metabolites, and interaction partners. Linking different aspects of a plants' phenotype (morphological and physiological traits, as well as metabolite profiles) with their potential ecological functions (biotic interactions) represents a much-needed step forward in both chemical and functional ecology. The effort the authors have put into compiling these datasets and establishing connections across sources is evident and deserves appreciation. The potential applications of this framework are manifold, and the examples provided on how the database can be used to explore research questions through knowledge paths convincingly demonstrate its value. The Introduction could be strengthened. At present, it feels somewhat long and diffuse in scope, which may make it harder for the reader to quickly grasp the importance of the contribution. Streamlining the text and sharpening the focus on the added value that linking different data sources provides would considerably improve clarity and help the reader more fully appreciate the strength of this novel approach. Minor comments: It would be helpful to specify more clearly what is meant by the "tremendous chemical diversity" that challenges metabolomic data analysis, and to clarify whether the "limited resources available to manage such complexity" refers to analytical limitations, data availability, or both. Similarly, the phrase "multi-level interaction data also lies locked in the guise of pairwise correlation metrics" could benefit from further explanation. As far as I am aware, the GLOBI database reports individual pairwise observations, which could already be seen as an effective way of documenting such interactions.

  3. Version published to 10.1093/gigascience/giag051
  4. Version published to 10.1101/2025.08.20.671289 on bioRxiv