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 dispersed and duplicated, resulting in highly branched, diversified, and promiscuous underlying metabolic networks. Additionally, knowledge of plant metabolic pathways remains scattered across disconnected resources, with limited interoperable annotations, and valuable experimental multi-omics datasets remain siloed and underused. Currently, 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. 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 across 643 species, including predictions of biosynthetic gene clusters. PlantMetWiki provides a foundational and extendible framework for community curation, enabling the future integration of experimental data on predicted biosynthetic pathways. This extension involves creating a community-supported validation schema to align and integrate multi-omics datasets, and a scoring method for predictions from multi-omics bioinformatics pipelines, interoperable with existing standards.
These developments will support an open-source knowledge base and a Linked Open Data community for scalable community annotation of pathway information, improved integration of multi-omics data, collaborative hypothesis generation on plant specialized biosynthesis, and prioritization of candidate pathway components of plant natural products.

Allele-specific expression analysis can reveal cis-regulatory differences (e.g., promoter variants, epigenetic changes) that cause imbalanced gene expression between haplotypes. Haplotype-resolved reference genomes and long-read RNA sequencing enable allele-specific expression analysis at gene and isoform-levels. However, existing tools are largely restricted to short-read RNA sequencing data and diploid organisms and are therefore not applicable for polyploid crop species.
We developed LongPolyASE (https://polyase.readthedocs.io/en/latest/), an end-to-end framework for allele-specific gene and isoform expression analysis in diploid and polyploid organisms using long-read RNA sequencing. It comprises three components: Syntelogfinder, for identifying syntenic genes across phased assemblies; longrnaseq, for transcript quantification, novel isoform discovery, and quality control; and PolyASE, for statistical testing of differential allelic expression, isoform usage, and haplotype-specific splicing. Applied to Oxford Nanopore RNA-seq data from diploid rice and autotetraploid potato, LongPolyASE identified cis-regulatory variation, tissue-specific trans-regulatory effects, and novel transcripts with potential relevance to plant development.
LongPolyASE addresses a methodological gap by enabling allele- and isoform-specific expression analysis in polyploid organisms. The framework is fully documented, openly available on GitHub and PyPI, and designed for FAIR and reproducible research. It directly benefits plant researchers working with complex genomes and supports the identification of regulatory variation and candidate targets in crop breeding.