Fu Xiangzheng, Liao Bo, Zhu Wen, Cai Lijun
College of Information Science and Engineering, Hunan University Changsha Hunan 410082 China
RSC Adv. 2018 Sep 3;8(54):30833-30841. doi: 10.1039/c8ra04138e. eCollection 2018 Aug 30.
MicroRNAs (miRNAs) are a family of short non-coding RNAs that play significant roles as post-transcriptional regulators. Consequently, various methods have been proposed to identify precursor miRNAs (pre-miRNAs), among which the comparative studies of miRNA structures are the most important. To measure and classify the structural similarity of miRNAs, we propose a new three-dimensional (3D) graphical representation of the secondary structure of miRNAs, in which an miRNA secondary structure is initially transformed into a characteristic sequence based on physicochemical properties and frequency of base. A numerical characterization of the 3D graph is used to represent the miRNA secondary structure. We then utilize a novel Euclidean distance method based on this expression to compute the distance of different miRNA sequences for the sequence similarity analysis. Finally, we use this sequence similarity analysis method to identify plant pre-miRNAs among three commonly used datasets. Results show that the method is reasonable and effective.
微小RNA（miRNA）是一类短链非编码RNA，作为转录后调节因子发挥着重要作用。因此，人们提出了各种方法来识别前体miRNA（pre-miRNA），其中对miRNA结构的比较研究最为重要。为了测量和分类miRNA的结构相似性，我们提出了一种新的miRNA二级结构三维（3D）图形表示方法，其中首先根据物理化学性质和碱基频率将miRNA二级结构转化为特征序列。利用3D图的数值特征来表示miRNA二级结构。然后，我们基于该表达式利用一种新颖的欧几里得距离方法来计算不同miRNA序列的距离，用于序列相似性分析。最后，我们使用这种序列相似性分析方法在三个常用数据集中识别植物pre-miRNA。结果表明该方法合理有效。
