Niu Mengting, Zhang Jun, Li Yanjuan, Wang Cankun, Liu Zhaoqian, Ding Hui, Zou Quan, Ma Qin
Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, Chengdu 610054, China.
Rehabilitation Department, Heilongjiang Province Land Reclamation Headquarters General Hospital, Harbin, China.
Comput Struct Biotechnol J. 2020 Apr 2;18:834-842. doi: 10.1016/j.csbj.2020.03.028. eCollection 2020.
Circular RNA (circRNA) plays an important role in the development of diseases, and it provides a novel idea for drug development. Accurate identification of circRNAs is important for a deeper understanding of their functions. In this study, we developed a new classifier, CirRNAPL, which extracts the features of nucleic acid composition and structure of the circRNA sequence and optimizes the extreme learning machine based on the particle swarm optimization algorithm. We compared CirRNAPL with existing methods, including blast, on three datasets and found CirRNAPL significantly improved the identification accuracy for the three datasets, with accuracies of 0.815, 0.802, and 0.782, respectively. Additionally, we performed sequence alignment on 564 sequences of the independent detection set of the third data set and analyzed the expression level of circRNAs. Results showed the expression level of the sequence is positively correlated with the abundance. A user-friendly CirRNAPL web server is freely available at http://server.malab.cn/CirRNAPL/.
环状RNA（circRNA）在疾病发展中发挥着重要作用，为药物研发提供了新思路。准确识别circRNA对于深入了解其功能至关重要。在本研究中，我们开发了一种新的分类器CirRNAPL，它提取circRNA序列的核酸组成和结构特征，并基于粒子群优化算法对极限学习机进行优化。我们在三个数据集上将CirRNAPL与包括blast在内的现有方法进行比较，发现CirRNAPL显著提高了三个数据集的识别准确率，准确率分别为0.815、0.802和0.782。此外，我们对第三个数据集独立检测集的564个序列进行了序列比对，并分析了circRNA的表达水平。结果表明，序列的表达水平与丰度呈正相关。用户可通过http://server.malab.cn/CirRNAPL/免费使用友好的CirRNAPL网络服务器。
