An overview and metanalysis of Machine and Deep Learning-Based CRISPR gRNA Design Tools

The CRISPR-Cas9 system has become the most promising and versatile tool for genetic manipulation applications. Albeit the technology has been broadly adopted by both academic and pharmaceutic societies, the activity (on-target) and specificity (off-target) of CRISPR-Cas9 are decisive factors for any application of the technology. Several <i>in silico</i> gRNA activity and specificity predicting models and web tools have been developed, making it much more convenient and precise for conducting CRISPR gene editing studies. In this review, we present an overview and comparative analysis of machine and deep learning (MDL)-based algorithms, which are believed to be the most effective and reliable methods for the prediction of CRISPR gRNA on- and off-target activities. As an increasing number of sequence features and characteristics are discovered and are incorporated into the MDL models, the prediction outcome is getting closer to experimental observations. We also introduced the basic principle of CRISPR activity and specificity and summarized the challenges they faced, aiming to facilitate the CRISPR communities to develop more accurate models for applying.

CRISPR-Cas9系统已成为遗传操作领域最具潜力且应用最为广泛的工具。尽管该技术已被学术界与制药界广泛采用，但CRISPR-Cas9的靶标活性（on-target）与脱靶特异性（off-target）仍是决定该技术各项应用效果的关键因素。目前已有多款计算机模拟（in silico）的gRNA活性与特异性预测模型及网页工具问世，使得CRISPR基因编辑研究的开展更为便捷精准。本综述对基于机器学习与深度学习（machine and deep learning, MDL）的算法进行了概述与对比分析，这类算法被认为是预测CRISPR gRNA靶标与脱靶活性最有效可靠的方法。随着越来越多的序列特征被发掘并融入MDL模型，预测结果与实验观测值的吻合度正不断提升。本综述还介绍了CRISPR靶标活性与特异性的基本原理，并总结了该领域当前面临的挑战，以期助力CRISPR研究社区开发出更为精准的应用模型。