Paicu Claudia, Mohorianu Irina, Stocks Matthew, Xu Ping, Coince Aurore, Billmeier Martina, Dalmay Tamas, Moulton Vincent, Moxon Simon
The Earlham Institute, Norwich Research Park, Norwich NR4 7UG, UK.
School of Computing Sciences.
Bioinformatics. 2017 Aug 15;33(16):2446-2454. doi: 10.1093/bioinformatics/btx210.
MicroRNAs are a class of ∼21-22 nt small RNAs which are excised from a stable hairpin-like secondary structure. They have important gene regulatory functions and are involved in many pathways including developmental timing, organogenesis and development in eukaryotes. There are several computational tools for miRNA detection from next-generation sequencing datasets. However, many of these tools suffer from high false positive and false negative rates. Here we present a novel miRNA prediction algorithm, miRCat2. miRCat2 incorporates a new entropy-based approach to detect miRNA loci, which is designed to cope with the high sequencing depth of current next-generation sequencing datasets. It has a user-friendly interface and produces graphical representations of the hairpin structure and plots depicting the alignment of sequences on the secondary structure.
We test miRCat2 on a number of animal and plant datasets and present a comparative analysis with miRCat, miRDeep2, miRPlant and miReap. We also use mutants in the miRNA biogenesis pathway to evaluate the predictions of these tools. Results indicate that miRCat2 has an improved accuracy compared with other methods tested. Moreover, miRCat2 predicts several new miRNAs that are differentially expressed in wild-type versus mutants in the miRNA biogenesis pathway.
miRCat2 is part of the UEA small RNA Workbench and is freely available from http://srna-workbench.cmp.uea.ac.uk/.
v.moulton@uea.ac.uk or s.moxon@uea.ac.uk.
Supplementary data are available at Bioinformatics online.
微小RNA是一类约21 - 22个核苷酸的小RNA，它们从稳定的发夹状二级结构中剪切而来。它们具有重要的基因调控功能，并参与包括真核生物发育时间、器官发生和发育在内的许多途径。有几种用于从下一代测序数据集中检测微小RNA的计算工具。然而，这些工具中的许多都存在高假阳性和假阴性率的问题。在此，我们提出了一种新的微小RNA预测算法miRCat2。miRCat2采用了一种基于新熵的方法来检测微小RNA位点，该方法旨在应对当前下一代测序数据集的高测序深度。它具有用户友好的界面，并能生成发夹结构的图形表示以及描绘二级结构上序列比对的图谱。
我们在多个动植物数据集上测试了miRCat2，并与miRCat、miRDeep2、miRPlant和miReap进行了比较分析。我们还使用微小RNA生物合成途径中的突变体来评估这些工具的预测结果。结果表明，与其他测试方法相比，miRCat2具有更高的准确性。此外，miRCat2预测了一些在微小RNA生物合成途径的野生型与突变体中差异表达的新微小RNA。
miRCat2是UEA小RNA工作台的一部分，可从http://srna-workbench.cmp.uea.ac.uk/免费获取。
v.moulton@uea.ac.uk或s.moxon@uea.ac.uk。
补充数据可在《生物信息学》在线获取。
