MicroRNAs are single-stranded noncoding RNAs known to down-regulate target genes at the protein or mRNA level. Computational prediction of targets is essential for elucidating the detailed functions of microRNA. However, prediction specificity and sensitivity of the existing algorithms still need to be improved to generate useful hypotheses for subsequent experimental testing. A new microRNA binding-site representation method was developed, which uses four symbols “[Formula: see text]”, “:”, “[Formula: see text]”, and “[Formula: see text]” (indicating paired, unpaired, insertion, and bulge, respectively) to represent the status of each nucleotide base pair in the microRNA binding site. New features were established with the information of every two adjacent symbols. There are 12 possible combinations and the frequency of each defines a set of novel and useful features. A comprehensive training dataset is constructed for mammalian microRNAs with positive targets obtained from the microRNA target depository in the miRTarbase, while negative targets were derived from pseudo-microRNA bindings. An SVM model was established using the training dataset and a new software called Min3 was developed. Performance of Min3 was assessed with intensively studied examples of miR-155 and miR-92a. Prediction results showed that Min3 can discover 47% of experimental conformed targets on average. The overlapping is above 20% on average when compared with TargetScan and miRanda. Annotations of the public microRNA datasets showed that there is a negative effect (up-regulation) of the Min3 targets for the knock out/down of miR-155 and miR-92a. Six top ranked targets were selected for validation by wet-lab experiments, and five of them showed a regulation effect. The Min3 can be a good alternative to current microRNA target discovery software. This tool is available at https://sourceforge.net/projects/mirt3 .

中文翻译：

---

Min3：使用改进的结合位点表示方法和支持向量机预测 microRNA 靶基因

MicroRNA 是单链非编码 RNA，已知可在蛋白质或 mRNA 水平上下调靶基因。目标的计算预测对于阐明 microRNA 的详细功能至关重要。然而，现有算法的预测特异性和敏感性仍需要改进，以便为后续的实验测试生成有用的假设。开发了一种新的 microRNA 结合位点表示方法，该方法使用四个符号“[Formula:see text]”、“:”、“[Formula:see text]”和“[Formula:see text]”（表示配对， unpaired, insert, 和bulge) 来表示microRNA结合位点中每个核苷酸碱基对的状态。利用每两个相邻符号的信息建立新的特征。有 12 种可能的组合，每种组合的频率定义了一组新颖且有用的特征。为哺乳动物 microRNA 构建了一个全面的训练数据集，阳性目标从 miRTarbase 中的 microRNA 目标库获得，而阴性目标则来自假 microRNA 结合。使用训练数据集建立了一个 SVM 模型，并开发了一个名为 Min3 的新软件。Min3 的性能通过对 miR-155 和 miR-92a 的深入研究示例进行评估。预测结果表明Min3平均可以发现47%的实验符合目标。与 TargetScan 和 miRanda 相比，重叠平均高于 20%。公共 microRNA 数据集的注释显示，Min3 靶标对 miR-155 和 miR-92a 的敲除/下调存在负面影响（上调）。通过湿实验室实验选择了六个排名靠前的目标进行验证，其中五个显示出调节作用。Min3 可以很好地替代当前的 microRNA 靶标发现软件。此工具可在 https://sourceforge.net/projects/mirt3 获得。