Rice Genomics and Genetics 2025, Vol.16, No.2, 106-115 http://cropscipublisher.com/index.php/rgg 113 resource constraints. For rice to be "sustainable", it must not only produce more, but also eat less and be able to withstand it. This requires us to truly understand the genetic mechanisms that control these traits, and functional genomics can fill this gap (Huang et al., 2013; Li et al., 2018). 7 Conclusion Rice functional genomics has been developing rapidly in recent years, but looking back, what is truly valuable is not only the progress of technology, but also our understanding of the mechanisms behind some key agronomic traits, which is much clearer than before. For long-standing problems such as yield, quality, and stress resistance, thousands of related genes have been cloned and partially annotated, relying on the genetic resources that have been established one after another, such as T-DNA insertion mutants and various mutant libraries. These resources actually laid the foundation as early as the last decade, and later promoted high-throughput research methods, allowing people to systematically see what each gene in the rice genome does. Of course, not all genes are easy to study. The functions of some genes are still vague, especially those without obvious phenotypes. However, with functional databases such as funRiceGenes, the currently known gene functions and the relationship network between them have been clarified, and subsequent research has a handle. Now even gene editing tools like CRISPR, or AI technologies like deep neural networks that were originally used for image recognition, have been used in rice research. They make the positioning of regulatory elements faster and more accurate, which was unthinkable before. As for where to go next, the research community actually has some consensus. For example, a large project like RICE2020 aims to figure out the function of every gene in the rice genome. It sounds like a science fiction goal, but the technical conditions are gradually maturing. The key is how to turn these research results into real-world traits that can be used, such as new varieties that are more drought-resistant, higher-yielding, or less prone to pests and diseases - these are what farmers care about most. Another point that is easily overlooked is the "black box" behind gene regulation - that is, epigenetics. It does not directly rewrite the DNA sequence, but it has a great impact on how plants develop and how they respond to stress. Incorporating this part of the research in the future may help us design breeding strategies more carefully, and even change the logic of traditional breeding. These advances ultimately depend on the continuous upgrading of functional genomics tools. Acknowledgments The publisher appreciates all the experts who participated in the peer review of this the manuscript. We appreciate the time you dedicated amidst your busy schedules to provide valuable insights and suggestions. Conflict of Interest Disclosure The authors affirm that this research was conducted without any commercial or financial relationships that could be construed as a potential conflict of interest. References Arora L., and Narula A., 2017, Gene editing and crop improvement using CRISPR-Cas9 system, Frontiers in Plant Science, 8: 1932. https://doi.org/10.3389/fpls.2017.01932 Buti M., Baldoni E., Formentin E., Milc J., Frugis G., Schiavo F., Genga A., and Francia E., 2019, A meta-analysis of comparative transcriptomic data reveals a set of key genes involved in the tolerance to abiotic stresses in rice, International Journal of Molecular Sciences, 20(22): 5662. https://doi.org/10.3390/ijms20225662 Delseny M., Salses J., Cooke R., Sallaud C., Regad F., Lagoda P., Guiderdoni E., Ventelon M., Brugidou C., and Ghesquière A., 2001, Rice genomics: present and future, Plant Physiology and Biochemistry, 39: 323-334. https://doi.org/10.1016/S0981-9428(01)01245-1 Dijk E., Auger H., Jaszczyszyn Y., and Thermes C., 2014, Ten years of next-generation sequencing technology, Trends in Genetics, 30(9): 418-426. https://doi.org/10.1016/j.tig.2014.07.001 Duitama J., Quintero J., Cruz D., Quintero C., Hubmann G., Foulquié-Moreno M., Verstrepen K., Thevelein J., and Tohme J., 2014, An integrated framework for discovery and genotyping of genomic variants from high-throughput sequencing experiments, Nucleic Acids Research, 42: e44-e44. https://doi.org/10.1093/nar/gkt1381
RkJQdWJsaXNoZXIy MjQ4ODYzNA==