Cotton Genomics and Genetics 2025, Vol.16, No.3, 107-116 http://cropscipublisher.com/index.php/cgg 114 affect fiber quality and yield, but also draw a "blueprint" of the regulatory network, knowing who is in front, who is behind, who is leading, and who cooperates. However, this alone is not enough. Understanding the mechanism is one thing, and whether it can be used in actual breeding is another. Therefore, the current research trend is more inclined to integrate the transcriptome, epigenome, and genome data together. Once the "components" such as QTL, regulatory factors, and expression modules are clearly disassembled, combined with molecular markers and algorithm models, we can have a more specific grasp of the entire development process. In fact, breeding has long begun to try to "rely on molecular technology". Now there are a number of stable QTLs, candidate genes, and superior alleles that can be used, and marker-assisted selection is also in use. New research is still in progress, such as trying to further improve the length and strength of fibers by accumulating superior alleles and then using genomic tools. This road is not easy, but it is becoming clearer and clearer. The more mature the technology and the more integrated the data, the more likely it is that new cotton varieties with high quality and high yield will be bred. Acknowledgments I extend my heartfelt appreciation to Dr. Meng for her guidance, insightful suggestions, and dedicated contributions during the study’s finalisation. 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 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(3): 699-713. https://doi.org/10.1111/pbi.12856 Bahrami B., Wolfien M., and Nikpour P., 2024, Integrated analysis of transcriptome and epigenome reveals ENSR00000272060 as a potential biomarker in gastric cancer, Epigenomics, 16(3): 159-173. https://doi.org/10.2217/epi-2023-0213 Bai F., and Scheffler J., 2024, Genetic and molecular regulation of cotton fiber initiation and elongation, Agronomy, 14(6):1208. https://doi.org/10.3390/agronomy14061208 Bao Y., Wei Y., Liu Y., Gao J., Cheng S., Liu G., You Q., Liu P., Lu Q., Li P., Zhang S., Hu N., Han Y., Liu S., Wu Y., Yang Q., Li Z., Ao G., Li, F., Wang K., Jiang J., Zhang T., Zhang W., and Peng R., 2023, Genome-wide chromatin accessibility landscape and dynamics of transcription factor networks during ovule and fiber development in cotton, BMC Biology, 21(1): 165. https://doi.org/10.1186/s12915-023-01665-4 Chen G., Liu Z., Li S., Liu L., Lu L., Wang Z., Mendu V., Li F., and Yang Z., 2023, Characterization of chromatin accessibility and gene expression reveal the key genes involved in cotton fiber elongation, Physiologia Plantarum, 175(4): e13972. https://doi.org/10.1111/ppl.13972 Fang D., Naoumkina M., and Kim H., 2018, Unraveling cotton fiber development using fiber mutants in the post‐genomic era, Crop Science, 58(6): 2214-2228. https://doi.org/10.2135/CROPSCI2018.03.0184 Jiao Y., Long Y., Xu K., Zhao F., Zhao J., Li S., Geng S., Gao W., Sun P., Deng X., Chen Q., Li C., and Qu Y., 2023, Weighted gene co-expression network analysis reveals hub genes for fuzz development in Gossypium hirsutum, Genes, 12(5): 753. https://doi.org/10.3390/genes14010208 Jin S., Zhang L., and Nie Q., 2020, scAI: an unsupervised approach for the integrative analysis of parallel single-cell transcriptomic and epigenomic profiles, Genome Biology, 21(1): 25. https://doi.org/10.1186/s13059-020-1932-8 Kartashov A., and Barski A., 2014, BioWardrobe: an integrated platform for analysis of epigenomics and transcriptomics data, Genome Biology, 16(1): 158. https://doi.org/10.1186/s13059-015-0720-3 Li Z., Wang P., You C., Yu J., Zhang X., Yan F., Ye Z., Shen C., Li B., Guo K., Liu N., Thyssen G., Fang D., Lindsey K., Zhang X., Wang M., and Tu L., 2020, Combined GWAS and eQTL analysis uncovers a genetic regulatory network orchestrating the initiation of secondary cell wall development in cotton, New Phytologist, 226(6): 1738-1752. https://doi.org/10.1111/nph.16468 Lister R., O’Malley R., Tonti-Filippini J., Gregory B., Berry C., Millar A., and Ecker J., 2008, Highly integrated single-base resolution maps of the epigenome in Arabidopsis, Cell, 133(3): 523-536. https://doi.org/10.1016/j.cell.2008.03.029
RkJQdWJsaXNoZXIy MjQ4ODYzNA==