用深度学习预测Cas13d基因编辑的精准度与脱靶风险
DeepFM-Crispr: Prediction of CRISPR On-Target Effects via Deep Learning
- 基于大语言模型生成进化与结构特征，提升sgRNA预测能力
- 在多个数据集上准确率超越现有方法，尤其擅长识别脱靶效应
- 适合基因编辑研究人员快速筛选高效且安全的sgRNA
自CRISPR-Cas9问世以来，这一通过短RNA引导序列实现精确基因组修饰的革命性技术，在多个领域广泛应用并持续推动投资。该技术的发展催生了其他CRISPR系统，如靶向RNA的CRISPR-Cas13。不同于靶向DNA的Cas9，Cas13靶向RNA，具有独特优势。本文聚焦于具有旁观切割活性（即激活后非特异性切割邻近RNA分子）的Cas13d变体，其特性对功能至关重要。我们提出DeepFM-Crispr，一种新型深度学习模型，用于预测Cas13d的在靶效率并评估脱靶效应。该模型利用大规模语言模型生成富含进化与结构信息的表示，显著提升对RNA二级结构和sgRNA整体效能的预测能力。基于Transformer的架构处理这些输入，输出预测效能分数。对比实验表明，DeepFM-Crispr不仅优于传统模型，还在预测准确性与可靠性上超越近期最先进的深度学习方法。
原文摘要 · Abstract (English)
Since the advent of CRISPR-Cas9, a groundbreaking gene-editing technology that enables precise genomic modifications via a short RNA guide sequence, there has been a marked increase in the accessibility and application of this technology across various fields. The success of CRISPR-Cas9 has spurred further investment and led to the discovery of additional CRISPR systems, including CRISPR-Cas13. Distinct from Cas9, which targets DNA, Cas13 targets RNA, offering unique advantages for gene modulation. We focus on Cas13d, a variant known for its collateral activity where it non-specifically cleaves adjacent RNA molecules upon activation, a feature critical to its function. We introduce DeepFM-Crispr, a novel deep learning model developed to predict the on-target efficiency and evaluate the off-target effects of Cas13d. This model harnesses a large language model to generate comprehensive representations rich in evolutionary and structural data, thereby enhancing predictions of RNA secondary structures and overall sgRNA efficacy. A transformer-based architecture processes these inputs to produce a predictive efficacy score. Comparative experiments show that DeepFM-Crispr not only surpasses traditional models but also outperforms recent state-of-the-art deep learning methods in terms of prediction accuracy and reliability.
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