Li Qinglian, Liu Guanqing, Bao Yu, Wu Yuechao, You Qi
Key Laboratory of Plant Functional Genomics of the Ministry of Education/Jiangsu Key Laboratory of Crop Genomics and Molecular Breeding/Co-Innovation Center for Modern Production Technology of Grain Crops College of Agriculture Yangzhou University Yangzhou 225009 China.
Jiangsu Xuzhou Sweet Potato Research Center Xuzhou 221131 China.
Appl Plant Sci. 2021 Apr 7;9(3):e11414. doi: 10.1002/aps3.11414. eCollection 2021 Mar.
MicroRNAs (miRNAs), endogenous non-coding RNA regulators, post-transcriptionally inhibit the expression of their target genes. Several tools have been developed for predicting annotated known miRNAs, but there is no consensus about how to select the most suitable method for any given species. In this study, eight miRNA prediction tools (mirnovo, miRPlant, miRDeep-P2, miRExpress, miRkwood, miRDeep2, miR-PREFeR, and sRNAbench) were selected for evaluation. High-throughput small RNA sequencing data from four plant species (including C and C species, and both monocots and dicots, i.e., , , , and ) were used for the analysis. The sensitivity, accuracy, area under the curve, consistency, duration, and RAM usage of the known miRNA predictions were evaluated for each tool. The miRNA annotations were obtained using miRBase and sRNAanno. Algorithms, such as random forest, BLAST, and receiver operating characteristic curves, were used to evaluate accuracy. Of the tools evaluated, sRNAbench was found to be the most accurate, miRDeep-P2 was the most sensitive, miRDeep-P2 was the fastest, and miRkwood had the highest memory usage. Due to its large genome size, only three tools were able to successfully predict known miRNAs in wheat (). Our results enable us to recommend the tool best suited to a variety of researcher needs, which we hope will reduce confusion and enhance future work.
微小RNA（miRNA）是内源性非编码RNA调节因子，可在转录后抑制其靶基因的表达。已经开发了几种工具来预测已注释的已知miRNA，但对于如何为任何给定物种选择最合适的方法尚无共识。在本研究中，选择了八种miRNA预测工具（mirnovo、miRPlant、miRDeep-P2、miRExpress、miRkwood、miRDeep2、miR-PREFeR和sRNAbench）进行评估。来自四种植物物种（包括C3和C4物种，以及单子叶植物和双子叶植物，即水稻、玉米、拟南芥和大豆）的高通量小RNA测序数据用于分析。对每个工具预测已知miRNA的灵敏度、准确性、曲线下面积、一致性、持续时间和随机存取存储器（RAM）使用情况进行了评估。使用miRBase和sRNAanno获得miRNA注释。使用随机森林、BLAST和受试者工作特征曲线等算法评估准确性。在所评估的工具中，发现sRNAbench最准确，miRDeep-P2最灵敏，miRDeep-P2最快，而miRkwood的内存使用量最高。由于小麦基因组规模较大，只有三种工具能够成功预测其已知miRNA。我们的结果使我们能够推荐最适合各种研究人员需求的工具，希望这将减少困惑并加强未来的工作。
