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Updated: Jul 16, 2026
09:43
Breeding by Design for Functional Rice with Genome Editing Technologies
Published on: January 3, 2025
AI-Guided DNA-Free and Genotype-Independent Genome Editing for Soybean Improvement.
Hye Jeong Kim1, Jia Chae1, Seong Ju Han1
1Department of Molecular Genetic Engineering, Dong-A University, Busan 49315, Republic of Korea.
Plants (Basel, Switzerland)
|July 15, 2026
Summary
Advancing soybean genetic improvement requires overcoming regeneration and editing efficiency hurdles. This review highlights DNA-free genome editing and AI-driven strategies for faster development of improved soybean varieties.
Area of Science:
- Agricultural Science
- Plant Biotechnology
- Genomics
Background:
- Soybean genetic improvement is vital for global food security but faces challenges like genotype-dependent regeneration and variable transformation efficiency.
- Stable transgene integration and regulatory concerns also limit current genetic enhancement strategies for this key crop.
Purpose of the Study:
- To review emerging DNA-free and genotype-independent genome-editing frameworks for soybean.
- To critically examine the current soybean genome-editing toolbox and identify persistent bottlenecks.
- To propose an integrated AI-to-field framework for accelerating soybean improvement.
Main Methods:
- Review of CRISPR-Cas9, Cas12a, base editing, and prime editing systems.
- Evaluation of DNA-free genome editing technologies (ribonucleoproteins, RNA-based systems, nanocarriers).
- Analysis of AI-assisted approaches integrating multi-omics data for target selection and prediction.
- Discussion of regeneration reprogramming strategies (BBM-WUS, GRF-GIF) to overcome cultivar-dependent barriers.
Main Results:
- Emerging DNA-free and genotype-independent genome-editing frameworks offer solutions to current soybean improvement constraints.
- AI-assisted approaches significantly enhance target prioritization, guide RNA design, and off-target prediction.
- Regeneration reprogramming strategies show promise in overcoming cultivar-dependent regeneration barriers.
- An integrated AI-to-field framework is proposed to streamline soybean improvement from discovery to deployment.
Conclusions:
- Convergent innovations in genome editing, AI, and regeneration reprogramming provide a foundation for accelerated soybean cultivar development.
- These advancements facilitate the creation of climate-resilient, nutritionally enhanced, and industry-ready soybean varieties.
- The proposed integrated framework offers a practical pathway for future soybean breeding efforts.
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