As a key technique for the CRISPR-Cas9 system, identification of single-guide RNAs (sgRNAs) on-target activity is critical for both theoretical research (investigation of RNA functions) and real-world applications (genome editing and synthetic biology). Because of its importance, several computational predictors have been proposed to predict sgRNAs on-target activity. All of these methods have clearly contributed to the developments of this very important field. However, they are suffering from certain limitations. We proposed two new methods called “sgRNA-PSM” and “sgRNA-ExPSM” for sgRNAs on-target activity prediction via capturing the long-range sequence information and evolutionary information using a new way to reduce the dimension of the feature vector to avoid the risk of overfitting. Rigorous leave-one-gene-out cross-validation on a benchmark dataset with 11 human genes and 6 mouse genes, as well as an independent dataset, indicated that the two new methods outperformed other competing methods. To make it easier for users to use the proposed sgRNA-PSM predictor, we have established a corresponding web server, which is available at http://bliulab.net/sgRNA-PSM/.

作为CRISPR-Cas9系统的一项关键技术，单导RNA（sgRNA）的靶向活性识别对于理论研究（RNA功能的研究）和实际应用（基因组编辑和合成生物学）均至关重要。由于其重要性，已提出了几种计算预测因子来预测sgRNA的靶向活性。所有这些方法显然促进了这一非常重要领域的发展。但是，他们受到某些限制。我们提出了两种新方法，分别称为“ sgRNA-PSM”和“ sgRNA-ExPSM”，通过捕获远距离序列信息和进化信息，从而减少特征向量的大小，从而避免了sgRNA的目标活性预测。过度拟合的风险。对具有11个人类基因和6个小鼠基因的基准数据集以及一个独立的数据集进行严格的“留一基因淘汰”交叉验证，表明这两种新方法的表现优于其他竞争方法。为了使用户更容易使用建议的sgRNA-PSM预测器，我们建立了相应的Web服务器，该服务器可从http://bliulab.net/sgRNA-PSM/获得。