Rice Genomics and Genetics 2025, Vol.16, No.3, 159-179 http://cropscipublisher.com/index.php/rgg 179 Lu Y., Wang J., Chen B., Mo S., Lian L., Luo Y., Ding D., Ding Y., Cao Q., Li Y., Li Y., Liu G., Hou Q., Cheng T., Wei J., Zhang Y., Chen G., Song C., Hu Q., Sun S., Fan G., Wang Y., Liu Z., Song B., Zhu J., Li H., and Jiang L., 2021, A donor-DNA-free CRISPR/Cas-based approach to gene knock-up in rice, Nature Plants, 7(11): 1445-1452. https://doi.org/10.1038/s41477-021-01019-4 Lu Y., Xu Y., and Li N., 2022, Early domestication history of asian rice revealed by mutations and genome-wide analysis of gene genealogies, Rice, 15(1): 11. https://doi.org/10.1186/s12284-022-00556-6 Qin P., Lu H., Du H., Wang H., Chen W., Chen Z., He Q., Ou S., Zhang H., Li X., Li X., Li Y., Liao Y., Gao Q., Tu B., Yuan H., Ma B., Wang Y., Qian Y., Fan S., Li W., Wang J., He M., Yin J., Li T., Jiang N., Chen X., Liang C., and Li S., 2021, Pan-genome analysis of 33 genetically diverse rice accessions reveals hidden genomic variations, Cell, 184: 3542-3558. https://doi.org/10.1016/j.cell.2021.04.046 Sedlazeck F.J., Rescheneder P., Smolka M., Fang H., Nattestad M., von Haeseler A., and Schatz M.C., 2018, Accurate detection of complex structural variations using single-molecule sequencing, Nature Methods, 15(6): 461-468. https://doi.org/10.1038/s41592-018-0001-7 Shang L., He W., Wang T., Yang Y., Xu Q., Zhao X., Yang L., Zhang H., Li X., Lv Y., Chen W., Cao S., Wang X., Zhang B., Liu X., Yu X., He H., Wei H., Leng Y., Shi C., Guo M., Zhang Z., Zhang B., Yuan Q., Qian H., Cao X., Cui Y., Zhang Q., Dai X., Liu C., Guo L., Zhou Y., Zheng X., Ruan J., Cheng Z., Pan W., and Qian Q., 2023, A complete assembly of the rice Nipponbare reference genome, Molecular Plant, 16(8): 1232-1236. https://doi.org/10.1016/j.molp.2023.08.003 Shang L., Li L., He H., Yuan Q., Song Y., Wei Z., Lin H., Hu M., Zhao F., Zhang C., Li Y., Gao H., Wang T., Liu X., Zhang H., Zhang Y., Cao S., Yu X., Zhang Y., Tan Y., Qin M., Ai C., Yang Y., Zhang B., Hu Z., Wang H., Lv Y., Wang Y., Ma J., Lu H., Wu Z., Liu S., Sun Z., Zhang H., Wang Y., Gao L., Li Z., Zhou Y., Li J., Zhu Z., Xiong G., Ruan J., and Qian Q., 2022, A super pan-genomic landscape of rice, Cell Research, 32(10): 878-896. https://doi.org/10.1038/s41422-022-00685-z Shi J., Tian Z., Lai J., and Huang X., 2022, Plant pan-genomics and its applications, Molecular plant, 16(1): 168-186. https://doi.org/10.1016/j.molp.2022.12.009 Shrestha A., Gonzales M., Ong P., Larmande P., Lee H., Jeung J., Kohli A., Chebotarov D., Mauleon R., Lee J., and McNally K., 2024, RicePilaf: a post-GWAS/QTL dashboard to integrate pangenomic, coexpression, regulatory, epigenomic, ontology, pathway, and text-mining information to provide functional insights into rice QTLs and GWAS loci, GigaScience, 13: giae013. https://doi.org/10.1093/gigascience/giae013 Song J.M., Guan Z., Hu J., Guo C., Zhang H., Wang S., Liu D., Wang B., Lu S.P., Zhou R., Xie W., Cheng Y., Zhang Y., Liu K., Yang Q., Chen L.L., and Guo L., 2020, Eight high-quality genomes reveal pan-genome architecture and ecotype differentiation of Brassica napus, Nature Plants, 6(1): 34-45. https://doi.org/10.1038/s41477-019-0577-7 Vahedi S., Momen M., Mousavi S., Banabazi M., Hasanvandi M., Bhatta M., Roudbar A., and Ardestani S., 2023, Population genetic analysis and scans for adaptation and contemporary selection footprints provide genomic insight into aus, indica and japonica rice cultivars diversification, Journal of Genetics, 102: 1-14. https://doi.org/10.1007/s12041-023-01440-y Wang J., Yang W., Zhang S., Hu T., Yuan H., Dong J., Chen L., Ma Y., Yang T., Zhou L., Chen J., Liu B., Li C., Edwards D., and Zhao J., 2023, A pangenome analysis pipeline provides insights into functional gene identification in rice, Genome Biology, 24(1): 19. https://doi.org/10.1186/s13059-023-02861-9 Woldegiorgis S., Wu T., Gao L., Huang Y., Zheng Y., Qiu F., Xu S., Tao H., Harrison A., Liu W., and He H., 2022, Identification of heat-tolerant genes in non-reference sequences in rice by integrating pan-genome, transcriptomics, and QTLs, Genes, 13(8): 1353. https://doi.org/10.3390/genes13081353 Wu D., Xie L., Sun Y., Huang Y., Jia L., Dong C., Shen E., Ye C., Qian Q., and Fan L., 2023, A syntelog-based pan-genome provides insights into rice domestication and de-domestication, Genome Biology, 24(1): 179. https://doi.org/10.1186/s13059-023-03017-5 Yang L., He W., Zhu Y., Lv Y., Li Y., Zhang Q., Liu Y., Zhang Z., Wang T., Wei H., Cao X., Cui Y., Zhang B., Chen W., He H., Wang X., Chen D., Liu C., Shi C., Liu X., Xu Q., Yuan Q., Yu X., Qian H., Li X., Zhang B., Zhang H., Leng Y., Zhang Z., Dai X., Guo M., Jia J., Qian Q., and Shang L., 2025, GWAS meta-analysis using a graph-based pan-genome enhanced gene mining efficiency for agronomic traits in rice, Nature Communications, 16(1): 3171. https://doi.org/10.1038/s41467-025-58081-1 Zhao Q., Feng Q., Lu H., Li Y., Wang A., Tian Q., Zhan Q., Lu Y., Zhang L., Huang T., Wang Y., Fan D., Zhao Y., Wang Z., Zhou C., Chen J., Zhu C., Li W., Weng Q., Xu Q., Wang Z., Wei X., Han B., and Huang X., 2018, Pan-genome analysis highlights the extent of genomic variation in cultivated and wild rice, Nature Genetics, 50(2): 278-284. https://doi.org/10.1038/s41588-018-0041-z
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