Wang Ying, Kuang Zheng, Li Lei, Yang Xiaozeng
Beijing Key Laboratory of Agricultural Genetic Resources and Biotechnology, Beijing Agro-Biotechnology Research Center, Beijing Academy of Agriculture and Forestry Sciences; State Key Laboratory of Protein and Plant Gene Research, Peking-Tsinghua Center for Life Sciences, School of Advanced Agricultural Sciences and School of Life Sciences, Peking University.
State Key Laboratory of Protein and Plant Gene Research, Peking-Tsinghua Center for Life Sciences, School of Advanced Agricultural Sciences and School of Life Sciences, Peking University;
J Vis Exp. 2020 Jan 21(155). doi: 10.3791/59864.
MicroRNAs (miRNAs) are 20- to 24-nucleotide (nt) endogenous small RNAs (sRNAs) extensively existing in plants and animals that play potent roles in regulating gene expression at the post-transcriptional level. Sequencing sRNA libraries by Next Generation Sequencing (NGS) methods has been widely employed to identify and analyze miRNA transcriptomes in the last decade, resulting in a rapid increase of miRNA discovery. However, two major challenges arise in plant miRNA annotation due to increasing depth of sequenced sRNA libraries as well as the size and complexity of plant genomes. First, many other types of sRNAs, in particular, short interfering RNAs (siRNAs) from sRNA libraries, are erroneously annotated as miRNAs by many computational tools. Second, it becomes an extremely time-consuming process for analyzing miRNA transcriptomes in plant species with large and complex genomes. To overcome these challenges, we recently upgraded miRDeep-P (a popular tool for miRNA transcriptome analyses) to miRDeep-P2 (miRDP2 for short) by employing a new filtering strategy, overhauling the scoring algorithm and incorporating newly updated plant miRNA annotation criteria. We tested miRDP2 against sequenced sRNA populations in five representative plants with increasing genomic complexity, including Arabidopsis, rice, tomato, maize and wheat. The results indicate that miRDP2 processed these tasks with very high efficiency. In addition, miRDP2 outperformed other prediction tools regarding sensitivity and accuracy. Taken together, our results demonstrate miRDP2 as a fast and accurate tool for analyzing plant miRNA transcriptomes, therefore a useful tool in helping the community better annotate miRNAs in plants.
微小RNA（miRNA）是长度为20至24个核苷酸（nt）的内源性小RNA（sRNA），广泛存在于动植物中，在转录后水平调控基因表达方面发挥着重要作用。在过去十年中，通过新一代测序（NGS）方法对sRNA文库进行测序已被广泛用于鉴定和分析miRNA转录组，从而使miRNA的发现迅速增加。然而，由于sRNA文库测序深度的增加以及植物基因组的大小和复杂性，植物miRNA注释面临两个主要挑战。首先，许多其他类型的sRNA，特别是来自sRNA文库的短干扰RNA（siRNA），被许多计算工具错误地注释为miRNA。其次，对于分析具有大而复杂基因组的植物物种中的miRNA转录组来说，这成为一个极其耗时的过程。为了克服这些挑战，我们最近通过采用新的过滤策略、全面改进评分算法并纳入新更新的植物miRNA注释标准，将miRDeep-P（一种流行的miRNA转录组分析工具）升级为miRDeep-P2（简称miRDP2）。我们用miRDP2测试了五种具有不断增加的基因组复杂性的代表性植物（包括拟南芥、水稻、番茄、玉米和小麦）的测序sRNA群体。结果表明，miRDP2能以非常高的效率处理这些任务。此外，在敏感性和准确性方面，miRDP2优于其他预测工具。综上所述，我们的结果表明miRDP2是一种用于分析植物miRNA转录组的快速且准确的工具，因此是帮助该领域更好地注释植物miRNA的有用工具。
