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Abstract：
Deletion is a crucial type of genomic structural variation and is associated with numerous genetic diseases. The advent of third-generation sequencing technology has facilitated the analysis of complex genomic structures and the elucidation of the mechanisms underlying phenotypic changes and disease onset due to genomic variants. Importantly, it has introduced innovative perspectives for deletion variants calling. Here we propose a method named Dual Attention Structural Variation (DASV) to analyze deletion structural variations in sequencing data. DASV converts gene alignment information into images and integrates them with genomic sequencing data through a dual attention mechanism. Subsequently, it employs a multi-scale network to precisely identify deletion regions. Compared with four widely used genome structural variation calling tools: cuteSV, SVIM, Sniffles and PBSV, the results demonstrate that DASV consistently achieves a balance between precision and recall, enhancing the F1 score across various datasets. The source code is available at https://github.com/deconvolution-w/DASV.
摘要：
缺失是基因组结构变异的关键类型，与许多遗传疾病有关。第三代测序技术的出现促进了复杂基因组结构的分析，并阐明了基因组变异导致的表型变化和疾病发作的潜在机制。重要的是,它为删除变体调用引入了创新的观点。在这里，我们提出了一种名为双注意力结构变异（DASV）的方法来分析测序数据中的缺失结构变异。DASV将基因比对信息转换为图像，并通过双重注意机制将其与基因组测序数据整合。随后，它采用多尺度网络来精确识别缺失区域。与四种广泛使用的基因组结构变异调用工具相比：cuteSV，SVIM,鼻烟和PBSV，结果表明，DASV始终在准确率和召回率之间取得平衡，增强各种数据集的F1分数。源代码可在https://github.com/deconvolution-w/DASV获得。
