Long-read-based de novo and somatic structural variant (SV) discovery remains challenging, necessitating genomic comparison between samples. We developed SVision-pro, a neural-network-based instance segmentation framework that represents genome-to-genome-level sequencing differences visually and discovers SV comparatively between genomes without any prerequisite for inference models. SVision-pro outperforms state-of-the-art approaches, in particular, the resolving of complex SVs is improved, with low Mendelian error rates, high sensitivity of low-frequency SVs and reduced false-positive rates compared with SV merging approaches.

中文翻译：

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使用 SVision-pro 发现从头和体细胞结构变异

基于长读的从头和体细胞结构变异（SV）发现仍然具有挑战性，需要样本之间的基因组比较。我们开发了 SVision-pro，这是一种基于神经网络的实例分割框架，可以直观地表示基因组到基因组级别的测序差异，并在没有任何推理模型先决条件的情况下发现基因组之间的 SV 比较。 SVision-pro 优于最先进的方法，特别是复杂 SV 的解析得到了改进，与 SV 合并方法相比，孟德尔错误率低，低频 SV 灵敏度高，误报率降低。