Journal of Tea Science Research, 2024, Vol.14, No.2, 79-91 http://hortherbpublisher.com/index.php/jtsr 89 secondary metabolite pathways, remains incomplete. There is also a need for more comprehensive studies on the epigenetics and noncoding RNAs in tea plants to fully understand their roles in gene regulation and trait expression. Furthermore, the genetic diversity within tea germplasm is vast, and more efforts are needed to explore and utilize this diversity for breeding purposes. The future of tea genomics research holds great promise. Advances in sequencing technologies and bioinformatics tools will likely lead to more complete and accurate genome assemblies, facilitating deeper insights into the genetic basis of tea quality and stress resistance. Functional genomic studies, including gene editing and transcriptome analysis, will be crucial for identifying and manipulating key genes to enhance desirable traits in tea plants. Additionally, integrating genomics with other omics approaches, such as metabolomics and proteomics, will provide a more holistic understanding of the molecular mechanisms governing tea plant biology. The development of high-throughput phenotyping platforms and the application of machine learning algorithms will further accelerate the breeding of improved tea varieties with enhanced quality and resilience to environmental stresses. Ultimately, these advancements will contribute to the sustainable production of high-quality tea, benefiting both producers and consumers worldwide. Acknowledgments The HortHerb Publisher appreciate the feedback from two anonymous peer reviewers on the manuscript of this study. Funding This work was supported by the National Natural Science Foundation of China [No. 32160077] and the Guizhou Academy of Agricultural Sciences Talent Special Project [2022-02 and 2023-02]. Conflict of Interest Disclosure The authors affirm that this research was conducted without any commercial or financial relationships that could be construed as a potential conflict of interest. References Appels R., Eversole K., Stein N., et al., 2018, Shifting the limits in wheat research and breeding using a fully annotated reference genome, Science, 361(6403): eaar7191. https://doi.org/10.1126/science.aar7191 PMid:30115783 Bansal G., Narta K., and Teltumbade M., 2018, Next-generation sequencing: technology, advancements, and applications, Bioinformatics: Sequences, Structures, Phylogeny, pp. 15-46. https://doi.org/10.1007/978-981-13-1562-6_2 PMCid:PMC5750229 Chin C., Peluso P., Sedlazeck F., Nattestad M., Concepcion G., Clum A., Dunn C., O’Malley R., Figueroa-Balderas R., Morales-Cruz A., Cramer G., Delledonne M., Luo C., Ecker J., Cantu D., Rank D., and Schatz M., 2016, Phased diploid genome assembly with single-molecule real-time sequencing, Nature Methods, 13(12): 1050-1054. https://doi.org/10.1038/nmeth.4035 PMid:27749838 PMCid:PMC5503144 Hazra A., Kumar R., Sengupta C., and Das S., 2020, Genome-wide SNP discovery from Darjeeling tea cultivars—their functional impacts and application toward population structure and trait associations, Genomics, 113(1): 66-78. https://doi.org/10.1016/j.ygeno.2020.11.028 PMid:33276009 Jain N., Taak Y., Choudhary R., Yadav S., Saini N., Vasudev S., and Yadava D., 2021, Advances and prospects of epigenetics in plants, Epigenetics and Metabolomics, pp. 421-444. https://doi.org/10.1016/b978-0-323-85652-2.00013-0 Koech R.K., Malebe P.M., Nyarukowa C., Mose R., Kamunya S.M., Loots T., and Apostolides Z., 2020, Genome-enabled prediction models for black tea ( Camellia sinensis) quality and drought tolerance traits, Plant Breeding, 139(5): 1003-1015. https://doi.org/10.1101/850792 Kong W., Jiang M., Wang Y., Chen S., Zhang S., Lei W., Chai K., Wang P., Liu R., and Zhang X., 2022, Pan-transcriptome assembly combined with multiple association analysis provides new insights into the regulatory network of specialized metabolites in the tea plant Camellia sinensis, Horticulture Research, 9: uhac100. https://doi.org/10.1093/hr/uhac100
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