Plant pan-transcriptomics: Integrating structural genomics, isoform diversity, and systems genetics for next-generation breeding

Pan-transcriptomics represents a transformative advance in plant systems biology, extending the pangenome concept to encompass transcriptional, isoform, and regulatory diversity across populations. This review integrates structural genomics, transcriptomics, and quantitative genetics to elucidate how genotype-dependent expression, alternative splicing, and transcript presence–absence variation (tPAV) collectively shape phenotypic plasticity and environmental adaptation in crops. We present a unified methodological framework that combines short- and long-read sequencing, genotype-specific reference transcript dataset (RTD) construction, and pan-RTD integration within linear and graph-based coordinate systems. Comparative analyses across cereals, legumes, and polyploid crops reveal pervasive isoform turnover, stress-inducible dispensable transcripts, and structural variants that drive regulatory diversity. Coupling isoform-resolved expression matrices with eQTL and sQTL mapping uncovers the cis- and trans-regulatory polymorphisms underlying complex traits, while network-level analyses demonstrate how core and variable isoforms remodel co-expression and signalling architecture under stress. Finally, we highlight translational applications in marker development, predictive modelling, and AI-assisted molecular breeding. Together, these advances establish crop pan-transcriptomics as a central systems framework linking genome architecture to transcriptional regulation, phenotypic diversity, and next-generation breeding innovation. Statement of Significance This review establishes a unified conceptual and methodological framework for crop pan-transcriptomics, integrating structural genomics, isoform diversity, and systems genetics to bridge genome architecture with transcriptional regulation and phenotypic diversity. Highlighting advances in genotype-specific reference transcript datasets, long-read sequencing, and isoform-resolved eQTL/sQTL mapping, it defines the analytical foundation for next-generation breeding and demonstrates how pan-transcriptomic approaches reveal dispensable and stress-responsive isoforms that enable predictive, AI-assisted crop improvement.