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Slenter, D.

Publications and source records attributed to Slenter, D..

3 recordsLinked to original sources

PlantMetWiki: a FAIR knowledge graph for plant metabolic pathway cross-species representation and integration

Plants produce a vast diversity of specialized metabolites with extensive potential ecological, agro-industrial, and pharmaceutical applications. Discovery of novel plant natural products relies on combining multi-omics evidence with biochemical transformations. However, pathway-level annotations are fragmented across individual species, databases, and publications, limiting comparative cross-species pathway analyses and systematic generation of hypotheses. To support integration and reuse of plant metabolic knowledge, we developed PlantMetWiki, a FAIR Linked Open Data semantically enriched knowledge graph built on infrastructure adapted from WikiPathways. Our approach extends on the highly curated pathway information from Plant Metabolic Network with crosslinks to biosynthetic gene clusters resources (MIBiG and plantiSMASH), metabolite annotations, and cross-species modelling. This way, our resource captures pathway genomic context, increases metabolomics data interoperability via federated queries, and supports cross-species analysis to identify annotation gaps. PlantMetWiki represents 1,162 plant metabolic pathways as Resource Description Framework (RDF) graphs, preserving pathway structure, literature provenance, annotations, and taxonomic information from PlantCyc 17.0. PlantMetWiki is distributed through a public SPARQL endpoint with open-source reproducible data transformation and validation workflows. By modelling pathways as a multispecies graph, PlantMetWiki enables comparative analyses across taxa, integration with external chemical knowledge resources through federated queries, and identification of metabolic, genomic, and chemical annotation gaps. As a result, PlantMetWiki provides a foundation for FAIR reuse and integration of plant pathway knowledge.

bioinformatics↗

Creating, curating and querying computer models of biological pathways for Inherited Metabolic Disorders

Collecting knowledge about Inherited Metabolic Disorders (IMDs) has the potential to support early diagnosis as well as foster research into treatment options. Biological pathway databases can be used to create an overview of relevant articles and books describing IMDs and collect, curate, and summarize knowledge about these disorders. Reuse of the information captured in these databases in research requires the knowledge to be accurate but also machine-readable. WikiPathways is a community-driven project to establish a machinereadable knowledge base of biological processes. We here describe how pathways models from WikiPathways were used to represent the underlying biological mechanisms of many IMDs collected by domain experts over a period of six years. This paper describes a standardized approach to depict IMDs in WikiPathways, shows the current limitations in creating machine-readable disease information, and introduces an approach to support data curation based on these machine-readable biological pathways. Furthermore, several SPARQL-queries were developed to analyze the biological content created in these models. Using this approach, 47 pathways were collected about 345 diseases, involving 877 metabolites, 421 annotated metabolic interactions, 262 genes, and 587 proteins related to these disorders.

bioinformatics↗

Wikidata as a FAIR knowledge graph for the life sciences

Wikidata is a community-maintained knowledge base that epitomizes the FAIR principles of Findability, Accessibility, Interoperability, and Reusability. Here, we describe the breadth and depth of biomedical knowledge contained within Wikidata, assembled from primary knowledge repositories on genomics, proteomics, genetic variants, pathways, chemical compounds, and diseases. We built a collection of open-source tools that simplify the addition and synchronization of Wikidata with source databases. We furthermore demonstrate several use cases of how the continuously updated, crowd-contributed knowledge in Wikidata can be mined. These use cases cover a diverse cross section of biomedical analyses, from crowdsourced curation of biomedical ontologies, to phenotype-based diagnosis of disease, to drug repurposing.

bioinformatics↗