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<p>Somatic variant discovery in tumors is crucial for clinical analysis, yet most existing methods are designed for short-read sequencing, with few developed specifically for long reads. Here we present Clair-Somatic (ClairS), a deep-learning-based somatic small-variant caller designed for long-read tumor–normal pairs. Trained on synthetic somatic variants with diverse coverages and variant allele fractions, ClairS accurately detects a wide range of somatic variants. Using the Nanopore Q20+ HCC1395–HCC1395BL dataset at 50/25× tumor/normal coverage, ClairS achieved F1 scores of 89.83% for single-nucleotide variations and 73.38% for indels; augmenting training with real cancer cell lines improved performance to 96.19% and 79.67%, respectively. Our findings indicate that improved read phasing enabled by long-read sequencing is key to accurate single-nucleotide variation detection, especially at low variant allele fractions. Through experiments across varied coverage, purity, contamination levels, multiple platforms and real cancer cell lines, we demonstrate that ClairS is a robust and reliable caller. ClairS is open source and available at <a href="https://github.com/HKU-BAL/ClairS">https://github.com/HKU-BAL/ClairS</a>.</p>

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ClairS: a deep-learning method for long-read tumor–normal pair somatic small variant calling

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年7月1日

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  • Zhenxian Zheng,
  • Lei Chen,
  • Junhao Su,
  • Xian Yu,
  • Minggao He,
  • Yan-Lam Lee,
  • Tak-Wah Lam,
  • Ruibang Luo

摘要

Somatic variant discovery in tumors is crucial for clinical analysis, yet most existing methods are designed for short-read sequencing, with few developed specifically for long reads. Here we present Clair-Somatic (ClairS), a deep-learning-based somatic small-variant caller designed for long-read tumor–normal pairs. Trained on synthetic somatic variants with diverse coverages and variant allele fractions, ClairS accurately detects a wide range of somatic variants. Using the Nanopore Q20+ HCC1395–HCC1395BL dataset at 50/25× tumor/normal coverage, ClairS achieved F1 scores of 89.83% for single-nucleotide variations and 73.38% for indels; augmenting training with real cancer cell lines improved performance to 96.19% and 79.67%, respectively. Our findings indicate that improved read phasing enabled by long-read sequencing is key to accurate single-nucleotide variation detection, especially at low variant allele fractions. Through experiments across varied coverage, purity, contamination levels, multiple platforms and real cancer cell lines, we demonstrate that ClairS is a robust and reliable caller. ClairS is open source and available at https://github.com/HKU-BAL/ClairS.