College of Life Sciences, Huzhou Teachers College.
Brief Bioinform. 2013 May;14(3):293-301. doi: 10.1093/bib/bbs040. Epub 2012 Jul 18.
Most plant microRNAs (miRNAs) perform their repressive regulation through target cleavages. The resulting slicing sites on the target transcripts could be mapped by sequencing of the 3'-cleavage remnants, called degradome sequencing. The high sequence complementarity between miRNAs and their targets has greatly facilitated the development of the target prediction tools for plant miRNAs. The prediction results were then subjected to degradome sequencing data-based validation, through which numerous miRNA-target interactions have been extracted. However, some drawbacks are unavoidable when using this forward approach. Essentially, a known list of plant miRNAs should be obtained in advance of target prediction and validation. This becomes an obstacle to discover novel miRNAs and their targets. Here, after reviewing the current available algorithms for reverse identification of miRNA-target pairs in plants, a case study was performed by using a newly established framework with adjustable parameters. In this workflow, integration of degradome and ARGONAUTE 1-enriched small RNA sequencing data was recommended to do a relatively comprehensive and reliable search. Besides, several computational algorithms such as BLAST, target plots and RNA secondary structure prediction were used. The results demonstrated the prevalent utility of the reversed approach for uncovering miRNA-target interactions in plants.
大多数植物 microRNAs（miRNAs）通过靶标切割来执行抑制性调节。靶转录本上的切割位点可以通过对 3'-切割残留物进行测序来映射，称为降解组测序。miRNAs 与其靶标的高度序列互补性极大地促进了植物 miRNAs 靶标预测工具的发展。预测结果随后通过基于降解组测序数据的验证进行验证，通过该验证提取了许多 miRNA-靶标相互作用。然而，在使用这种正向方法时，不可避免地会存在一些缺点。本质上，在进行靶标预测和验证之前，应该事先获得已知的植物 miRNAs 列表。这成为发现新的 miRNAs 和它们的靶标的障碍。在这里，在回顾了当前植物中用于反向识别 miRNA-靶对的可用算法之后，通过使用新建立的可调整参数的框架进行了案例研究。在这个工作流程中，建议整合降解组和富含 ARGONAUTE 1 的小 RNA 测序数据，以进行相对全面和可靠的搜索。此外，还使用了 BLAST、靶标图谱和 RNA 二级结构预测等几种计算算法。结果表明，该反向方法在揭示植物中的 miRNA-靶标相互作用方面具有普遍的应用价值。
