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<p>Accurate identification of somatic small variants in tumors plays a crucial role in cancer diagnosis. Various somatic mutation callers have been developed; however, existing methods face limitations in modeling mapping information of flanking genomic sites (genomic sites adjacent to a somatic site) that influence the state of the somatic site. Additionally, they are unable to analyze inter-site interactions within the context sequence centered around a somatic site or appropriately weigh the effects of flanking genomic sites on the somatic site. To address these limitations, the Transformer model is utilized to develop TransSSVs for detecting somatic small variants. The core functionality of TransSSVs relies on the multi-head attention mechanism, which generates a reliable representation of interactions between a candidate somatic site and its flanking genomic sites within the context sequence. TransSSVs effectively extract mapping features of various genomic sites in the context sequence to enhance prediction accuracy. Benchmarking experiments demonstrate that TransSSVs exhibit robust performance when compared with state-of-the-art methods on well-characterized real and simulated tumor datasets. Furthermore, the contributions of flanking genomic sites to the detection of somatic sites are assessed, and attention weight patterns for positive and negative somatic sites are analyzed.</p>

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TransSSVs: a Transformer-based deep learning model for accurate detection of somatic small variants in paired tumor and normal sequencing data

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年7月16日

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  • Jing Meng,
  • Jiangyuan Wang,
  • Jingze Liu,
  • Wenkai Song,
  • Ming Li,
  • Aiping Wu,
  • Taijiao Jiang

摘要

Accurate identification of somatic small variants in tumors plays a crucial role in cancer diagnosis. Various somatic mutation callers have been developed; however, existing methods face limitations in modeling mapping information of flanking genomic sites (genomic sites adjacent to a somatic site) that influence the state of the somatic site. Additionally, they are unable to analyze inter-site interactions within the context sequence centered around a somatic site or appropriately weigh the effects of flanking genomic sites on the somatic site. To address these limitations, the Transformer model is utilized to develop TransSSVs for detecting somatic small variants. The core functionality of TransSSVs relies on the multi-head attention mechanism, which generates a reliable representation of interactions between a candidate somatic site and its flanking genomic sites within the context sequence. TransSSVs effectively extract mapping features of various genomic sites in the context sequence to enhance prediction accuracy. Benchmarking experiments demonstrate that TransSSVs exhibit robust performance when compared with state-of-the-art methods on well-characterized real and simulated tumor datasets. Furthermore, the contributions of flanking genomic sites to the detection of somatic sites are assessed, and attention weight patterns for positive and negative somatic sites are analyzed.