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<p>Resistance (R) genes play a critical role in defending against pathogenic infections, and identifying novel R genes is a key component of resistance breeding. However, current computational methods face significant limitations when predicting R genes from ultra-long, highly repetitive genomic sequences. In this study, we propose a deep learning framework, CLAP-HMM, which employs a CNN-LSTM-Attention fusion strategy to extract gene expression patterns from long DNA sequences and accurately predict R genes. Our approach introduces protein function hints as biological priors to guide the HMM module in structural state modeling, enabling precise identification of R gene boundaries. Comparative experiments demonstrate that CLAP-HMM outperforms existing methods in discovering more potential novel R genes, offering promising prospects for uncovering biologically meaningful functional genes and advancing our understanding of disease resistance mechanisms.</p>

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CLAP-HMM: a biologically constrained deep learning framework for resistance gene prediction in long DNA sequences

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年12月12日

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  • Liuyan Wang,
  • Yingfan Xu,
  • Xuemei Guan,
  • Shanchun Yan

摘要

Resistance (R) genes play a critical role in defending against pathogenic infections, and identifying novel R genes is a key component of resistance breeding. However, current computational methods face significant limitations when predicting R genes from ultra-long, highly repetitive genomic sequences. In this study, we propose a deep learning framework, CLAP-HMM, which employs a CNN-LSTM-Attention fusion strategy to extract gene expression patterns from long DNA sequences and accurately predict R genes. Our approach introduces protein function hints as biological priors to guide the HMM module in structural state modeling, enabling precise identification of R gene boundaries. Comparative experiments demonstrate that CLAP-HMM outperforms existing methods in discovering more potential novel R genes, offering promising prospects for uncovering biologically meaningful functional genes and advancing our understanding of disease resistance mechanisms.