Liu Bin, Luo Zhihua, He Juan
School of Computer Science and Technology, Beijing Institute of Technology, Beijing, China; Advanced Research Institute of Multidisciplinary Science, Beijing Institute of Technology, Beijing, China.
Affiliated Shenzhen Maternity & Child Healthcare Hospital, Southern Medical University, Shenzhen, Guangdong, China.
Mol Ther Nucleic Acids. 2020 Jun 5;20:323-330. doi: 10.1016/j.omtn.2020.01.029. Epub 2020 Jan 31.
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，单导向RNA（sgRNA）靶向活性的鉴定对于理论研究（RNA功能研究）和实际应用（基因组编辑与合成生物学）都至关重要。鉴于其重要性，人们已经提出了几种计算预测方法来预测sgRNA的靶向活性。所有这些方法都为这个非常重要的领域的发展做出了明确贡献。然而，它们存在一定的局限性。我们提出了两种名为“sgRNA-PSM”和“sgRNA-ExPSM”的新方法，通过以一种新的方式捕捉长程序列信息和进化信息来减少特征向量的维度，从而避免过拟合风险，以预测sgRNA的靶向活性。在一个包含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数据集上进行的严格留一基因交叉验证表明，这两种新方法优于其他竞争方法。为了方便用户使用所提出的sgRNA-PSM预测器，我们建立了一个相应的网络服务器，可在http://bliulab.net/sgRNA-PSM/上访问。
