Cotton Genomics and Genetics 2025, Vol.16, No.4, 202-209 http://cropscipublisher.com/index.php/cgg 208 But then again, genomic data has been increasing, and the people involved in the research are from different disciplines. In addition, new technologies are constantly leaning towards breeding, so things are not that simple. Sometimes you will find that if a data interface is not matched, the whole process will be stuck. Although the direction of precision breeding is clear, there is still a lot to do if it is to be truly implemented. AI, machine learning, and high-throughput tools sound very "high-tech", but in order to make them really work, they have to be embedded in the platform little by little. Prediction, screening, and analysis are not done manually by people, but are automatically run by the system. This is the next goal. To achieve global applicability? That is even more of a systematic project. The unified data format and the mutual recognition of platforms are not something that any team can solve alone. Besides, the investment of resources must keep up. Basic work such as genome splicing cannot be expected to be done once and for all; if the interface is not friendly, no one will want to use the tool no matter how powerful it is. Looking back now, these databases and platforms are not only built by technology, they have become the "infrastructure" of modern cotton breeding. Technology is changing, and people are changing, but as long as scientists are willing to share and work together, these platforms can continue to move forward and help us cope with future climate, yield, and other uncertain problems. In the end, the goal is still the same old goal: to breed new cotton varieties that can handle things, have high yields, and are easy to manage. Acknowledgments I thank Mr. Li for his careful review of an earlier draft, whose comments enhanced the rigor of the argument. Conflict of Interest Disclosure The author affirms that this research was conducted without any commercial or financial relationships that could be construed as a potential conflict of interest. References Blindenbach J., Kang J., Hong S., Karam C., Lehner T., and Gürsoy G., 2024, SQUiD: ultra-secure storage and analysis of genetic data for the advancement of precision medicine, Genome Biology, 25(1): 314. https://doi.org/10.1186/s13059-024-03447-9 Chen L., Aziz M., Mohammed N., and Jiang X., 2018, Secure large-scale genome data storage and query, Computer Methods and Programs in Biomedicine, 165: 129-137. https://doi.org/10.1016/j.cmpb.2018.08.007 Cheng K., Hou Y., and Wang L., 2023, Secure similar sequence query over multi-source genomic data on cloud, IEEE Transactions on Cloud Computing, 11(3): 2803-2819. https://doi.org/10.1109/TCC.2022.3228906 Conaty W., Broughton K., Egan L., Li X., Li Z., Liu S., Llewellyn D., MacMillan C., Moncuquet P., Rolland V., Ross B., Sargent D., Zhu Q., Pettolino F., and Stiller W., 2022, Cotton breeding in australia: meeting the challenges of the 21st century, Frontiers in Plant Science, 13: 904131. https://doi.org/10.3389/fpls.2022.904131 Dahlquist, J., Nelson S., and Fullerton S., 2023, Cloud-based biomedical data storage and analysis for genomic research: landscape analysis of data governance in emerging NIH-supported platforms, Human Genetics and Genomics Advances, 4(3): 100196. https://doi.org/10.1016/j.xhgg.2023.100196 Dai F., Chen J., Zhang Z., Liu F., Li J., Zhao T., Hu Y., Zhang T., and Fang L., 2022, COTTONOMICS: a comprehensive cotton multi-omics database, Database, 2022: baac080. https://doi.org/10.1093/database/baac080 Dove E., Joly Y., Tassé A., Kaye P., Burton P., Chisholm R., Fortier I., Goodwin P., Harris J., Hveem K., Kaye J., Kent A., Knoppers B., Lindpaintner K., Little J., Riegman P., Ripatti S., Stolk R., Knoppers M., Bobrow M., Cambon-Thomsen A., Dressler L., Joly Y., Kato K., Rodriguez L., McPherson T., Nicolàs P., Ouellette F., Romeo-Casabona C., Sarin R., Wallace S., Wiesner G., Wilson J., Zeps N., Simkevitz H., De Rienzo A., and Knoppers B., 2014, Genomic cloud computing: legal and ethical points to consider, European Journal of Human Genetics, 23(10): 1271-1278. https://doi.org/10.1038/ejhg.2014.196 Issac A., Ebrahimi A., Velni J., and Rains G., 2023, Development and deployment of a big data pipeline for field-based high-throughput cotton phenotyping data, Smart Agricultural Technology, 5: 100265. https://doi.org/10.1016/j.atech.2023.100265 Kun W., He S., and Zhu Y., 2025, Cotton2035: from genomics research to optimized breeding, Molecular Plant, 18(2): 298-312. https://doi.org/10.1016/j.molp.2025.01.010 Langmead B., and Nellore A., 2018, Cloud computing for genomic data analysis and collaboration, Nature Reviews Genetics, 19(4): 208-219.
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