Bioscience Evidence 2025, Vol.15, No.5, 219-227 http://bioscipublisher.com/index.php/be 225 7 Conclusion In recent years, many new tools have emerged in cotton genomics. Common ones include high-throughput sequencing platforms, genome assembly and annotation software, molecular marker development methods, genome-wide association studies (GWAS), as well as multi-omics databases (such as CottonGen, CottonFGD, COTTONOMICS) and phenotypic analysis systems based on deep learning. These tools have been widely applied in cotton research, such as genome sequencing, gene function annotation, trait mapping, mining of stress-resistant genes, molecular breeding and high-throughput phenotypic analysis, etc. They make genetic improvement faster and more precise. With the development of bioinformatics, cotton research is shifting from traditional phenotypic selection to genome-driven precision breeding. Genomic sequencing and big data analysis have revealed the complex polyploid genomic structure of cotton and also demonstrated the relationship between functional gene regulatory networks and traits. These achievements have provided new ideas for high-yield, high-quality and stress-resistant breeding. The application of gene editing tools such as CRISPR/Cas enables researchers to precisely modify target genes, significantly accelerating the breeding of new varieties. Meanwhile, the application of deep learning and artificial intelligence in phenotypic identification, trait prediction and data mining has also promoted the automation and intelligence of research. In the future, cotton genomics will still rely on these tools to further promote precise breeding and sustainable production. High-quality reference genomes and pan-genome resources have laid the foundation for trait improvement and diversity conservation. The combination of multi-omics integration, machine learning and big data analysis will help achieve molecular design breeding of complex traits. This not only increases output, but also improves quality and stress resistance. New technologies such as gene editing and molecular marker selection will also accelerate the breeding of disease-resistant and stress-resistant varieties, helping the cotton industry cope with climate change and resource pressure, and move towards green, efficient and sustainable development. Acknowledgments I wish to express my sincere gratitude to the anonymous reviewers for their insightful feedback and constructive suggestions, which have greatly enhanced the quality of this paper. 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 Amarasinghe S., Ritchie M., and Gouil Q., 2021, long-read-tools.org: an interactive catalogue of analysis methods for long-read sequencing data, GigaScience, 10(2): giab003. https://doi.org/10.1093/gigascience/giab003 Amarasinghe S., Su S., Dong X., Zappia L., Ritchie M., and Gouil Q., 2020, Opportunities and challenges in long-read sequencing data analysis, Genome Biology, 21: 30. https://doi.org/10.1186/s13059-020-1935-5 Ashraf J., Zuo D., Wang Q., Malik W., Zhang Y., Abid M., Cheng H., Yang Q., and Song G., 2018, Recent insights into cotton functional genomics: progress and future perspectives, Plant Biotechnology Journal, 16: 699-713. https://doi.org/10.1111/pbi.12856 Chen Z., Grover C., Li P., Wang Y., Nie H., Zhao Y., Wang M., Liu F., Zhou Z., Wang X., Cai X., Wang K., Wendel J., and Hua J., 2017, Molecular evolution of the plastid genome during diversification of the cotton genus, Molecular Phylogenetics and Evolution, 112: 268-276. https://doi.org/10.1016/j.ympev.2017.04.014 De Coster W., Weissensteiner M., and Sedlazeck F., 2021, Towards population-scale long-read sequencing, Nature Reviews. Genetics, 22: 572-587. https://doi.org/10.1038/s41576-021-00367-3 De Koning W., Miladi M., Hiltemann S., Heikema A., Hays J., Flemming S., van den Beek M., Mustafa D., Backofen R., Grüning B., and Stubbs A., 2020, NanoGalaxy: Nanopore long-read sequencing data analysis in Galaxy, GigaScience, 9(10): giaa105. https://doi.org/10.1093/gigascience/giaa105 Gao W., Long L., Tian X., Xu F., Liu J., Singh P., Botella J., and Song C., 2017 Genome editing in cotton with the CRISPR/Cas9 system, Frontiers in Plant Science, 8: 1364. https://doi.org/10.3389/fpls.2017.01364
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