Rice Genomics and Genetics 2025, Vol.16, No.3, 159-179 http://cropscipublisher.com/index.php/rgg 165 Insertions: segments of DNA present in one genome that are absent in another (often called presence/absence variants when comparing against a reference). An insertion could range from a few base pairs (e.g., transposon insertion) to thousands of base pairs harboring one or more genes. Deletions: the opposite of insertions, where a segment found in the reference is missing in another genome. Large deletions can knock out genes or regulatory regions, sometimes with phenotypic consequences. (Insertions and deletions are together often termed “indels,” especially when smaller than a fewkb). Inversions: DNA segments that flip their direction without changing the actual gene content. Still, they may disrupt gene order and interfere with how genes are regulated or recombined. Translocations: happen when a piece of DNA shifts to a new location-either within the same chromosome or across different ones. In rice, such swaps between subspecies are rare, but a few have been found between wild and cultivated varieties. Copy Number Variations (CNVs): segments that are present in multiple copies (duplications) or have reduced copy (including complete deletion) in one genome relative to another. CNVs can involve gene duplications or deletions and are an important source of dosage variation for genes. Presence/absence variants (PAVs)-essentially large insertions/deletions encompassing whole genes-are a particularly important class of SV in pan-genomes. These PAVs lead to genes that are entirely missing from some genomes but present in others, contributing heavily to the dispensable genome component. Overall, structural variants account for a substantial proportion of genetic differences among rice accessions and often have larger effect sizes on phenotype than SNPs, given that they can disrupt or duplicate entire genes. 4.2 Computational tools and pipelines for SV detection To find structural differences in rice genomes, one practical way is to compare whole genome assemblies. When you have good-quality genome data from different rice varieties, you can line them up and spot the parts that don’t match. Tools like MUMmer or nucmer help compare each genome to a standard one like Nipponbare, showing where pieces are missing or extra. This method has revealed thousands of insertions and deletions in past studies. It can also uncover more complex changes, like when genome sections are flipped or moved by checking for disrupted alignment patterns. Another approach is read-based SV calling, which operates on sequencing reads mapped to a reference genome. Traditional SV callers for short reads (e.g., Pindel, DELLY, LUMPY) use patterns such as discordant read pairs or split reads to infer deletions, inversions, or duplications. However, short reads have limited power for complex or repetitive SVs. The advent of long reads improved this: long-read SV callers (such as Sniffles and PBHoney) can directly map long reads and identify SV signatures with higher sensitivity and specificity. Sedlazeck et al. (2018) introduced Sniffles, which leverages PacBio reads to accurately detect complex SVs, demonstrating far more insertions/deletions in a human genome than previously known. In rice, long-read data from multiple varieties have been similarly processed to identify tens of thousands of SVs that short-read analyses missed. Beyond individual discovery, genotyping SVs across populations is crucial. Tools like SVType and graph-based genotypers use known SV coordinates to screen other accessions for presence/absence. The incorporation of SVs into a pan-genome graph enables mapping short reads from many accessions onto all alternate alleles, facilitating efficient genotyping. For example, Hickey et al. (2020) showed that the VG toolkit could genotype thousands of SVs in large panels using a variation graph. These tools and pipelines, combined in workflows, form the backbone of rice pan-genome projects, allowing researchers to systematically detect and compare SVs across hundreds or thousands of genomes.
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