Plant specialized metabolites are a major source of innovative pharmaceuticals, novel foods, and biobased natural products. However, resolving the metabolic pathways that biosynthesize these compounds remains a key scientific challenge, as plant biosynthetic genes are often genomically dispersed and duplicated, resulting in highly branched, diversified, and promiscuous underlying metabolic networks. Additionally, knowledge of plant metabolic pathway knowledge remains scattered across disconnected resources, with limited interoperable annotations, and valuable experimental multi-omics datasets remain siloed and underused. To address these gaps, we developed the Linked Open Data knowledge graph PlantMetWiki (https://plantmetwiki.bioinformatics.nl/), the first semantically enriched knowledge base for querying experimentally validated pathway information for plant biosynthetic reactions, including predictions of biosynthetic gene clusters. Still, researchers lack an open, standardized, and reusable AI-ready format for integrating new datasets with community knowledge. Additionally, there is no framework to systematically link computational evidence from bioinformatics tools and paired plant transcriptomics-metabolomics datasets to predict or validate pathway-level knowledge.

Therefore, in this BioHackathon project, we aim to:

1. Co-develop a community-supported Minimum Information Standard (MIS) for predicted plant pathway knowledge representation;
2. Design validation schemas to integrate experimental multi-omics datasets and predictions in alignment with the MIS;
3. Establish methods to align pathway-related predictions across bioinformatics pipelines and approaches for scoring and prioritization of these predictions based on evidence strength, provenance, and community-defined criteria.

These outcomes will enable scalable community annotation of pathway information, improved integration of multi-omics data, prioritization of candidate pathway components, and more reproducible and transparent bioinformatics analyses.