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<p>Variant calling in segmental duplications is challenging for short-read sequencing because of ambiguous read origins. We present SDrecall, a method for sensitive variant detection in these regions. Upon constructing a network of homologous sequences, SDrecall realigns reads to each segmental duplication from its homologous counterparts. Realignments are phased and assembled into haplotypes via graph-based algorithms, followed by integer linear programming to retain the two most plausible haplotypes. Tested against long-read benchmarks, SDrecall achieved 95% sensitivity, while maintaining manageable false positives for short variants. SDrecall thus offers significant value for molecular diagnosis in terms of causal mutation detection within homologous regions.</p>

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SDrecall: a sensitive approach for variant detection in segmental duplications

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年1月12日

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  • Xing Tian Yang,
  • Chun Hing She,
  • CaiCai Zhang,
  • Daniel Leung,
  • Jing Yang,
  • Koon-Wing Chan,
  • Jaime S. Rosa Duque,
  • Yu Lung Lau,
  • Wanling Yang

摘要

Variant calling in segmental duplications is challenging for short-read sequencing because of ambiguous read origins. We present SDrecall, a method for sensitive variant detection in these regions. Upon constructing a network of homologous sequences, SDrecall realigns reads to each segmental duplication from its homologous counterparts. Realignments are phased and assembled into haplotypes via graph-based algorithms, followed by integer linear programming to retain the two most plausible haplotypes. Tested against long-read benchmarks, SDrecall achieved 95% sensitivity, while maintaining manageable false positives for short variants. SDrecall thus offers significant value for molecular diagnosis in terms of causal mutation detection within homologous regions.