Advances and Paradigms in Modern Plant Bioinformatics: A Comprehensive Review

Plant bioinformatics has undergone a paradigm shift from simple gene discovery and sequence alignment to AI-driven multi-omics integration, graph pangenomics, and precision molecular breeding. The intrinsic hyper-complexity of plant genomes—characterized by high polyploidy, extensive heterozygosity, massive repetitive element burdens, and dynamic structural variations—has mandated the invention of specialized assembly algorithms, statistically robust annotation pipelines, and high-dimensional deep learning architectures. This comprehensive review synthesizes core computational methodologies across de novo polyploid sequence assembly (Overlap-Layout-Consensus vs. De Bruijn Graphs), structural and evidence-based functional annotation, epigenomic landscape mapping (WGBS, ATAC-Seq), graph-based pangenomics, single-cell multi-omics resolution, and transcriptomic network modeling. Furthermore, we evaluate state-of-the-art transformer-based Genomic Language Models (GLMs), structural biology integrations (AlphaFold3/ESMFold), and specialized plant databases. Finally, key computational bottlenecks, infrastructure scaling considerations, and actionable computational frameworks for accelerating climate-resilient crop breeding are critically evaluated.