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<p>CRISPR-Cas12a (Cpf1) offers distinct advantages for genome editing due to its flexible, T-rich PAM recognition. However, variable cleavage efficiency—modulated by sequence context and epigenetic features—remains a challenge, with existing tools facing challenges in modeling the high-order interactions between multimodal features. Here, we present DeepCas12a, a hybrid deep learning framework integrating Convolutional Neural Networks (CNNs) and a Vision Transformer (ViT) encoder to capture both local sequence motifs and long-range dependencies. The model fuses DNA sequence data with epigenetic profiles (DNA methylation and chromatin accessibility) in an end-to-end architecture. Benchmarked on an independent test set, DeepCas12a outperformed state-of-the-art predictors, achieving an Average Precision of 0.783, an AUC of 0.868, and a Spearman correlation of 0.630. Furthermore, interpretability analysis via saliency maps confirms the model captures biologically relevant features, including PAM specificity and seed region sensitivity, facilitating rational guide RNA design.</p>

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DeepCas12a: a hybrid deep learning framework for accurate AsCas12a efficiency prediction from sequence and epigenetic information

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年5月30日

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  • Yiming Shi,
  • Junkai Yin,
  • Shurui Ning,
  • Jinling Yuan,
  • Degang Yang,
  • Guohui Chuai

摘要

CRISPR-Cas12a (Cpf1) offers distinct advantages for genome editing due to its flexible, T-rich PAM recognition. However, variable cleavage efficiency—modulated by sequence context and epigenetic features—remains a challenge, with existing tools facing challenges in modeling the high-order interactions between multimodal features. Here, we present DeepCas12a, a hybrid deep learning framework integrating Convolutional Neural Networks (CNNs) and a Vision Transformer (ViT) encoder to capture both local sequence motifs and long-range dependencies. The model fuses DNA sequence data with epigenetic profiles (DNA methylation and chromatin accessibility) in an end-to-end architecture. Benchmarked on an independent test set, DeepCas12a outperformed state-of-the-art predictors, achieving an Average Precision of 0.783, an AUC of 0.868, and a Spearman correlation of 0.630. Furthermore, interpretability analysis via saliency maps confirms the model captures biologically relevant features, including PAM specificity and seed region sensitivity, facilitating rational guide RNA design.