LAYLA: development of a comprehensive and cross-sample program for detecting structural variants and its application to citrus cultivars

Structural variants (SVs) are genomic mutations that are typically 50 bp or larger. Given their larger scale compared to single nucleotide polymorphisms and small insertions and deletions, SVs are expected to be associated with various traits in several crops and fruit species. They can also be used to identify plant cultivars. However, it is challenging to detect SVs using short-read next-generation sequencing (NGS), which, until re‍cently, has been the mainstream method, due to its short read length compared to SVs. In recent years, long-read NGS, which generates reads exceeding the length of SVs, has made SV detection more feasible. To take advantage of this, we developed LAYLA (Large indel AnalYzer for muLti-sAmple), a pipeline program designed to comprehensively detect and visualize SVs across multiple samples using long-read data. Here, we applied LAYLA to 13 citrus founder cultivars used in Japanese breeding and Citrus unshiu Marc. We identified SVs at 59,983 positions in the reference genome. This analysis revealed both common and cultivar-specific SVs. Furthermore, we designed primers targeting nine selected SVs and conducted experimental val‍idation, confirming the presence of SVs detected by LAYLA. In the future, LAYLA can be applied to other plant species to detect SVs.