Plant genome assembly and annotation.

Area of Science:

• Plant genomics
• Bioinformatics
• Computational biology

Background:

• Advancements in long-read sequencing and assembly algorithms enable complete plant genome assemblies.
• The bottleneck in plant genomics has shifted from genome assembly to accurate annotation and interpretation.
• Current annotation relies on ab initio methods and evidence-based frameworks integrating diverse omics data.

Purpose of the Study:

• To highlight the transformative era in plant genome biology driven by new technologies.
• To discuss the challenges and opportunities in plant genome annotation.
• To explore the future directions and potential impact of AI and multi-omics in plant genomics.

Main Methods:

• Utilizing long-read sequencing technologies for chromosome-scale genome assemblies.
• Employing improved assembly algorithms and scaffolding strategies.
• Integrating RNA sequencing, chromatin accessibility, methylation, and 3D genome data for evidence-based annotation.
• Leveraging artificial intelligence (AI)-driven gene predictors and large-scale orthology networks.

Main Results:

• Feasibility of gapless, haplotype-resolved genome assemblies, including for polyploid species.
• Rapid advancement in evidence-based genome annotation frameworks.
• Redefinition of ab initio gene finding through AI-driven predictors.
• Improved functional inference via large-scale orthology networks.

Conclusions:

• The next frontier involves extending annotation to regulatory and structural elements using single-cell and multi-omics.
• Integration of AI, multi-omics, and large language models will standardize and automate annotation workflows.
• These innovations promise to accelerate plant biology discovery, conservation, and crop improvement.