Rice Genomics and Genetics 2025, Vol.16, No.5, 245-253 http://cropscipublisher.com/index.php/rgg 248 For genes like OsSTOMAGEN that control stomatal density, once the cis-element is rewritten, not only will drought tolerance be affected, but water use efficiency will also change accordingly (Karavolias et al., 2024). These variations are often targeted by natural selection, which is why they are always given priority in breeding. 3.3 SV-driven gene loss and duplication in ecological adaptation processes Some SVS do not immediately change the function of a gene, but they quietly "replicate" a few copies or simply "delete" the original, which can also have a considerable impact on environmental adaptation. Gene duplication can double the expression levels of some stress response genes, while the loss of certain negative regulatory factors is sometimes beneficial to plants (Fuentes et al., 2019). In the high-frequency SV region, such "variant burdens" not only failed to become a burden but also gave rise to many new alleles, some of which were even used for agronomic improvement. What is more complex is that behind many SVS, transpositional factors are also involved, and even serial repetitions occur. This dynamic change in structure enables the regulatory network of rice to evolve particularly rapidly and be more adaptable to various extreme climates (Lou et al., 2017; Li, 2024). 4 Technologies and Analytical Methods for SV Detection 4.1 Advantages of long-read sequencing technologies (PacBio, ONT) in SV identification Some structural variations (SVS) are hidden in large areas of repetition, and conventional short-read techniques can easily "overlook" them. This problem has been precisely solved by long-read sequencing platforms such as PacBio and ONT. PacBio HiFi read length not only has a wide coverage but also high base accuracy, capable of identifying insertions and deletions as small as a few dozen bases. Although ONT has a slightly lower single-base accuracy rate, it can pull out continuous read lengths of 2 Mb or even longer, and shows more advantages when dealing with large areas of repetition or complex regions (Lang et al., 2020; Harvey et al., 2023). Of course, both platforms have their own shortcomings. However, when used in combination, they are highly complementary, not only enhancing the completeness of genome assembly but also making SV annotations more accurate. 4.2 Genome-wide SV reconstruction and pan-genome construction in diverse populations The construction of a pan-genome is no longer as simple as "piecing together a set of reference genomes". Especially in crops with extremely strong germplasm diversity such as rice, conducting long-read sequencing based on different populations can more comprehensively explore different types of SVS such as insertions, deletions, duplications, and PAVs (presence/deletion variations). The emergence of map-based pan-genomes and super pan-genomes gives us the opportunity to concatenate linaly-specific SVS and construct a dynamically changing whole-genome structure map, which is not only convenient for GWAS localization (Shang et al., 2022), It also provides a new perspective for studying the evolutionary paths and trait effects of these variations in the population. To truly understand the relationship between SV and phenotypes, this step is inevitable. 4.3 Advances in bioinformatics tools for SV annotation, visualization, and functional prediction There are more and more tools and their functions are becoming increasingly detailed. Nowadays, in order to accurately detect SV, many research teams have begun to use software specifically optimized for long reads such as SVIM-asm, Sniffles, cuteSV, pbsv or DeBreak to handle complex or multiallelic events (Chen et al., 2023). If it is a group-level analysis, a tool like PanPop can significantly enhance the integration efficiency of SV data. As for subsequent annotations and visualization, some databases and platforms are also being continuously updated. For example, the Rice Super Pan-Genome resource integrates rich SV annotation information and browsing interfaces (Shang et al., 2022). It is worth noting that the latest functional prediction methods are no longer confined to coding regions but have also begun to attempt to analyze non-coding regulatory regions, which is crucial for understanding how SV affects gene expression and even phenotypic traits. 5 Epigenetic and Transcriptomic Regulation Mediated by SVs 5.1 Impact of SVs on chromatin accessibility and histone modifications In rice, the active state of chromatin is not always static, and changes in histone modifications are often accompanied by the emergence of structural variations (SV). Histone markers like H3K4me3 and H3K27me3 have long been regarded as key markers for regulating transcription. Studies have found that the distribution and
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