Cotton Genomics and Genetics 2025, Vol.16, No.3, 107-116 http://cropscipublisher.com/index.php/cgg 113 developmental stages. Without this, it is difficult for us to link specific epigenetic marks with specific gene regulation or trait expression. In other words, the clues are not absent, but they are "blurred" (Naoumkina and Kim, 2023). 7.3 Technical and computational challenges in data integration and interpretation It is actually very difficult to integrate different types of data, such as transcriptome, epigenome, and proteome data. The cotton genome is very complex and polyploid, and the large amount of data puts a lot of pressure on subsequent comparison, analysis, and understanding. We now need more powerful computational tools and unified analysis processes to process data from these different sources, resolve the contradictions between them, and finally extract meaningful biological information from them (Wang et al., 2020; Prasad et al., 2022). 8 Future Directions and Technological Advances 8.1 Single-cell omics for precise mapping of regulatory states during fiber development In fact, it is not that no one doubts the existence of many regulatory processes, but the previous technology was too crude to grasp such a fine level. Now it is different. The emergence of single-cell omics has finally broken the fuzziness of "average value". It can directly depict the true picture of gene expression and epigenetic state at the level of a single cell-even the switching between different developmental stages can be distinguished in detail (Wang et al., 2020; Bai and Scheffler, 2024). In the past, the data given by mixed samples were too "fuzzy" to see which type of cells played a leading role. These new technologies solve the problems of "not being able to locate" and "not being able to see the details". Especially when single-cell transcriptome and spatial genomic data are combined, those regulatory factors "where they work and when they start to take charge" can finally be seen at a glance. This is of great significance for combing the regulatory chain of the entire cotton fiber development. 8.2 CRISPR-based functional validation of candidate regulatory elements Not all candidate genes are worth further study, and not every regulatory region is really useful. This is when CRISPR comes in handy. Tools such as CRISPR/Cas9 and base editors are now used very skillfully in cotton research (Zhu and Luo, 2024). But don't get me wrong, the goal of these editing tools is not to "improve traits" immediately, but to verify. Whether the suspicious objects screened out by omics are the "key players" in regulating fiber development, using CRISPR to knock it out or overexpress it will quickly show changes. This is what Wen et al. (2023) did. Its significance is more than just verification. Once these direct evidences are available, the subsequent breeding direction will be clearer. Especially when breeding cotton varieties with better fiber quality, CRISPR can help you avoid detours and screen faster. 8.3 Machine learning for predictive modeling of gene regulatory networks Not all candidate genes are worth further study, and not every regulatory region is really useful. This is when CRISPR comes in handy. Tools such as CRISPR/Cas9 and base editors are now used very skillfully in cotton research. But don't get me wrong, the goal of these editing tools is not to "improve traits" immediately, but to verify. Whether the suspicious objects screened out by omics are the "key players" in regulating fiber development, using CRISPR to knock it out or overexpress it will quickly show changes. This is what Wen et al. (2023) did. Its significance is more than just verification. Once these direct evidences are available, the subsequent breeding direction will be clearer. Especially when breeding cotton varieties with better fiber quality, CRISPR can help you avoid detours and screen faster. 9 Concluding Remarks It's not that no one has paid attention to how cotton fiber develops, but in the early years, the tools and data were not detailed enough. In recent years, the development of transcriptomics and epigenomics has indeed allowed researchers to see deeper. From the transcriptome data, we can see which genes are active in the stages of initiation, elongation and maturity; and the research at the epigenetic level has gradually revealed how chromatin "controls" these expression rhythms. Of course, these two methods have limited effects when used alone. What can really break down complex problems is to look at them together. Through integrated analysis, we can not only identify the key genes that
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