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<p>Somatic structural variations (SVs) are critical in cancer genomes, yet their detection from long-read sequencing remains challenging due to alignment errors in repetitive regions. We develop SVScope, leveraging full-length reads and local graph-genome optimization with a random forest strategy to improve somatic SV calling. We also provide ScopeVIZ, a companion pipeline for visualizing read clustering at breakpoints. Across seven benchmark cell lines sequenced with ONT and PacBio platforms, as well as simulated datasets, SVScope consistently outperforms state-of-the-art methods, achieving up to 23.64% improvement in F1-score. Using SVScope, we validate 32 somatic SVs, expanding the ground-truth dataset by 47.06%.</p>

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SVScope improves somatic structural variations detection via graph-genome optimization

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年4月23日

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  • Kailing Tu,
  • Qilin Zhang,
  • Yang Li,
  • Yucong Li,
  • Lanfang Yuan,
  • Jing Wang,
  • Jie Tang,
  • Lin Xia,
  • Wei Huang,
  • Dan Xie

摘要

Somatic structural variations (SVs) are critical in cancer genomes, yet their detection from long-read sequencing remains challenging due to alignment errors in repetitive regions. We develop SVScope, leveraging full-length reads and local graph-genome optimization with a random forest strategy to improve somatic SV calling. We also provide ScopeVIZ, a companion pipeline for visualizing read clustering at breakpoints. Across seven benchmark cell lines sequenced with ONT and PacBio platforms, as well as simulated datasets, SVScope consistently outperforms state-of-the-art methods, achieving up to 23.64% improvement in F1-score. Using SVScope, we validate 32 somatic SVs, expanding the ground-truth dataset by 47.06%.