Bioscience Evidence 2025, Vol.15, No.5, 219-227 http://bioscipublisher.com/index.php/be 224 the processing and sharing of large-scale data (Liu et al., 2014; de Koning et al., 2020; Oh et al., 2020; Ren et al., 2022). These platforms not only enable researchers without a computing background to use them, but also promote data standardization and repeatability, which is of great help to the popularization and cooperation of cotton genomics. 6.3 Integration of multi-omics and machine learning for predictive breeding If multi-omics data (genomic, transcriptomic, epigenomic, metabolomic, etc.) are combined with machine learning and deep learning, they can be better applied to precision breeding and trait prediction. Machine learning can be used for dimensionality reduction, regulatory network construction, candidate gene screening, and can also help establish an association model of genotype - phenotype - environment. These methods provide tools for molecular design breeding of complex traits (Oh et al., 2020; Yang et al., 2021; 2022; Yan and Wang, 2022). Platforms like CottonMD have integrated multi-omics data and analysis tools, providing significant support for cotton genetic improvement and functional gene research (Yang et al., 2022). 6.4 Need for international collaboration and open-source tool development As the volume of data and the difficulty of analysis increase, international cooperation and the development of open-source tools become even more important. Open databases and tools (such as long-read-tools.org) facilitate resource sharing, method standardization and community communication (Amarasinghe et al., 2020; Amarasinghe et al., 2021; Thriveni et al., 2024) (Figure 2). Global cooperation helps to enhance data interoperability, promote the development of new algorithms and platforms, and accelerate the application and innovation of cotton genomics worldwide (Thriveni et al., 2024). Figure 2 Example use of the Tools tab from long-read-tools.org. A. The custom toolbar for the page. B. Drop-down "Sort By" menu. C. Drop-down "Filter by categories" menu, which allows users to select multiple options by clicking on an item or typing the word in the text box. D. Drop-down Filter by "technologies" menu, which allows users to select multiple options by clicking on an item or typing the word in the text box. When multiple categories or technologies are selected, the website returns the intersection, not the union; i.e., a tool has to satisfy all the requirements to be reported (Adopted from Amarasinghe et al., 2021)
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