Pan Xiaoyong, Fang Yi, Li Xianfeng, Yang Yang, Shen Hong-Bin
Institute of Image Processing and Pattern Recognition, Shanghai Jiao Tong University, and Key Laboratory of System Control and Information Processing, Ministry of Education of China, Shanghai, 200240, China.
Key laboratory of Carcinogenesis and Translational Research, Peking University Cancer Hospital, Beijing, 100142, China.
BMC Genomics. 2020 Dec 9;21(1):884. doi: 10.1186/s12864-020-07291-6.
RNA-binding proteins (RBPs) play crucial roles in various biological processes. Deep learning-based methods have been demonstrated powerful on predicting RBP sites on RNAs. However, the training of deep learning models is very time-intensive and computationally intensive.
Here we present a deep learning-based RBPsuite, an easy-to-use webserver for predicting RBP binding sites on linear and circular RNAs. For linear RNAs, RBPsuite predicts the RBP binding scores with them using our updated iDeepS. For circular RNAs (circRNAs), RBPsuite predicts the RBP binding scores with them using our developed CRIP. RBPsuite first breaks the input RNA sequence into segments of 101 nucleotides and scores the interaction between the segments and the RBPs. RBPsuite further detects the verified motifs on the binding segments gives the binding scores distribution along the full-length sequence.
RBPsuite is an easy-to-use online webserver for predicting RBP binding sites and freely available at http://www.csbio.sjtu.edu.cn/bioinf/RBPsuite/ .
RNA结合蛋白（RBPs）在各种生物学过程中发挥着关键作用。基于深度学习的方法已被证明在预测RNA上的RBP位点方面很强大。然而，深度学习模型的训练非常耗时且计算量大。
在此，我们展示了基于深度学习的RBPsuite，这是一个易于使用的网络服务器，用于预测线性和环状RNA上的RBP结合位点。对于线性RNA，RBPsuite使用我们更新的iDeepS预测其RBP结合分数。对于环状RNA（circRNAs），RBPsuite使用我们开发的CRIP预测其RBP结合分数。RBPsuite首先将输入的RNA序列分成101个核苷酸的片段，并对片段与RBPs之间的相互作用进行评分。RBPsuite进一步检测结合片段上经过验证的基序，给出沿全长序列的结合分数分布。
RBPsuite是一个易于使用的在线网络服务器，用于预测RBP结合位点，可在http://www.csbio.sjtu.edu.cn/bioinf/RBPsuite/免费获取。
