Yang Yanpeng, Li Jian, Zou Quan, Ruan Yaoping, Feng Hailin
School of Mathematics and Computer science, Zhejiang A&F University, Hangzhou 311300, China.
Yangtze Delta Region Institute (Quzhou), University of Electronic Science and Technology of China, Quzhou 324000, China.
Comput Struct Biotechnol J. 2023 Oct 16;21:5039-5048. doi: 10.1016/j.csbj.2023.10.018. eCollection 2023.
The CRISPR/Cas9 system has significantly advanced the field of gene editing, yet its clinical application is constrained by the considerable challenge of off-target effects. Although numerous deep learning models for off-target prediction have been proposed, most struggle to effectively extract the nuanced features of guide RNA (gRNA) and DNA sequence pairs and to mitigate information loss during data transmission within the model. To address these limitations, we introduce a novel Hybrid Neural Network (HNN) model that employs a parallelized network structure to fully extract pertinent features from different positions and types of bases in the sequence to minimize information loss. Notably, this study marks the first application of word embedding techniques to extract information from sequence pairs that contain insertions and deletions (Indels). Comprehensive evaluation across diverse datasets indicates that our proposed model outperforms existing state-of-the-art prediction methods in off-target prediction. The datasets and source codes supporting this study can be found at https://github.com/Yang-k955/CRISPR-HW.
CRISPR/Cas9系统极大地推动了基因编辑领域的发展，但其临床应用受到脱靶效应这一巨大挑战的限制。尽管已经提出了许多用于脱靶预测的深度学习模型，但大多数模型难以有效提取引导RNA（gRNA）和DNA序列对的细微特征，并难以减少模型内数据传输过程中的信息损失。为了解决这些局限性，我们引入了一种新型混合神经网络（HNN）模型，该模型采用并行网络结构，从序列中不同位置和类型的碱基充分提取相关特征，以尽量减少信息损失。值得注意的是，本研究标志着词嵌入技术首次应用于从包含插入和缺失（Indels）的序列对中提取信息。在不同数据集上的综合评估表明，我们提出的模型在脱靶预测方面优于现有的最先进预测方法。支持本研究的数据集和源代码可在https://github.com/Yang-k955/CRISPR-HW上找到。
