College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China.
PLoS One. 2013;8(1):e53685. doi: 10.1371/journal.pone.0053685. Epub 2013 Jan 9.
MicroRNAs (miRNAs) are a class of small (19-25 nt) non-coding RNAs. This important class of gene regulator downregulates gene expression through sequence-specific binding to the 3'untranslated regions (3'UTRs) of target mRNAs. Several computational target prediction approaches have been developed for predicting miRNA targets. However, the predicted target lists often have high false positive rates. To construct a workable target list for subsequent experimental studies, we need novel approaches to properly rank the candidate targets from traditional methods. We performed a systematic analysis of experimentally validated miRNA targets using functional genomics data, and found significant functional associations between genes that were targeted by the same miRNA. Based on this finding, we developed a miRNA target prioritization method named mirTarPri to rank the predicted target lists from commonly used target prediction methods. Leave-one-out cross validation has proved to be successful in identifying known targets, achieving an AUC score up to 0. 84. Validation in high-throughput data proved that mirTarPri was an unbiased method. Applying mirTarPri to prioritize results of six commonly used target prediction methods allowed us to find more positive targets at the top of the prioritized candidate list. In comparison with other methods, mirTarPri had an outstanding performance in gold standard and CLIP data. mirTarPri was a valuable method to improve the efficacy of current miRNA target prediction methods. We have also developed a web-based server for implementing mirTarPri method, which is freely accessible at http://bioinfo.hrbmu.edu.cn/mirTarPri.
微小 RNA（miRNAs）是一类小的（19-25nt）非编码 RNA。作为重要的基因调控因子，miRNAs 通过与靶 mRNA 的 3'非翻译区（3'UTRs）特异性结合来下调基因表达。已经开发了几种计算靶标预测方法来预测 miRNA 靶标。然而，预测的靶标列表通常具有较高的假阳性率。为了构建用于后续实验研究的可行靶标列表，我们需要从传统方法中正确排列候选靶标的新方法。我们使用功能基因组学数据对经过实验验证的 miRNA 靶标进行了系统分析，发现被同一 miRNA 靶向的基因之间存在显著的功能关联。基于这一发现，我们开发了一种 miRNA 靶标优先级排序方法，命名为 mirTarPri，用于对常用靶标预测方法的预测靶标列表进行排序。留一法交叉验证成功地识别了已知靶标，达到了高达 0.84 的 AUC 评分。在高通量数据中的验证证明 mirTarPri 是一种无偏的方法。将 mirTarPri 应用于对六种常用靶标预测方法的优先级排序结果进行排序，使我们能够在优先级候选列表的顶部找到更多的阳性靶标。与其他方法相比，mirTarPri 在黄金标准和 CLIP 数据中表现出色。mirTarPri 是一种提高当前 miRNA 靶标预测方法效果的有价值的方法。我们还开发了一个基于网络的服务器来实现 mirTarPri 方法，可在 http://bioinfo.hrbmu.edu.cn/mirTarPri 上免费访问。
