International Journal of Molecular Zoology, 2025, Vol.15, No.2, 58-68 http://animalscipublisher.com/index.php/ijmz 60 When comparing the length distribution of genes, CDS, exons and introns between A. fulica and many closely related mollusks, several characteristics can be identified. It shows little difference in the distribution of genes and CDS length from most species, indicating that the overall gene structure is relatively conserved. The exon length distribution is highly concentrated, which indicates that the structure of the coding region is relatively stable. Introns are slightly longer, which may be related to their high proportion of repetitive sequences (Figure 1). In addition, the gene families related to functions, like respiration, summer sleep, immune defense, and mucus secretion have expanded, which also explains its survival advantages in terrestrial environments. Compared with other gastropods, such as Biomphalaria glabrata and Pomacea canaliculata, the genome of the African land snail has some unique "personalities", including a higher proportion of repetitive sequences. And linea-specific gene expansion (Guo et al., 2019; Toma et al., 2023). Figure 1 Length distribution comparison of genes (A), CDSs (B), exons (C), and introns (D) for A. fulica to those in the closely related mollusk species A. californica, B.glabrata, C. gigas, L. gigantea, P. yessoensis, and O. bimaculoides (Adopted from Guo et al., 2019) 3 Structural Variations and Their Landscape 3.1 Classification and detection of SVs inL. fulica Structural Variations (SVs), covering various types of genomic structural changes such as deletions, insertions, inversions, duplications and translocations, involve fragments longer than 50 bases (Guan and Sung, 2016; Merot et al., 2020; Laufer et al., 2023). These structural variations may disrupt gene functions, alter regulatory regions, and play significant roles in phenotypic diversity and adaptive evolution (Merot et al., 2020; Laufer et al., 2023). With the development of next-generation sequencing (NGS) and long-read sequencing technologies, the detection capability of SV has been significantly enhanced. Tools such as Sniffles2, based on long-read data, can identify complex structural variations, including chimeric variations and population-level variations, with high accuracy and speed (Smolka et al., 2024). The SV recognition process usually integrates multiple algorithms to improve sensitivity and specificity, as different detectors vary in recognition ability and are affected by sequencing platforms and library construction methods (Guan and Sung, 2016; Ho et al., 2019; Smolka et al., 2024). That is to say, the selection of structural variation detection tools and processes should be optimized and adjusted based on the research objective and the complexity of the target genome (Guan and Sung, 2016; Smolka et al., 2024).
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