Institute of Molecular Biology and Biotechnologies, 4108 Markovo, Bulgaria.
Department of Plant Physiology and Molecular Biology, University of Plovdiv, 4000 Plovdiv, Bulgaria.
Int J Mol Sci. 2022 Apr 2;23(7):3979. doi: 10.3390/ijms23073979.
Plant small RNAs (sRNAs) are a heterogeneous group of noncoding RNAs with a length of 20-24 nucleotides that are widely studied due to their importance as major regulators in various biological processes. sRNAs are divided into two main classes-microRNAs (miRNAs) and small interfering RNAs (siRNAs)-which differ in their biogenesis and functional pathways. Their identification and enrichment with new structural variants would not be possible without the use of various high-throughput sequencing (NGS) techniques, allowing for the detection of the total population of sRNAs in plants. Classifying sRNAs and predicting their functional role based on such high-performance datasets is a nontrivial bioinformatics task, as plants can generate millions of sRNAs from a variety of biosynthetic pathways. Over the years, many computing tools have been developed to meet this challenge. Here, we review more than 35 tools developed specifically for plant sRNAs over the past few years and explore some of their basic algorithms for performing tasks related to predicting, identifying, categorizing, and quantifying individual sRNAs in plant samples, as well as visualizing the results of these analyzes. We believe that this review will be practical for biologists who want to analyze their plant sRNA datasets but are overwhelmed by the number of tools available, thus answering the basic question of how to choose the right one for a particular study.
植物小 RNA（sRNA）是一组长度为 20-24 个核苷酸的非编码 RNA，由于它们作为各种生物过程的主要调节剂的重要性而被广泛研究。sRNA 分为两个主要类别——microRNAs（miRNAs）和 small interfering RNAs（siRNAs）——它们在生物发生和功能途径上有所不同。如果不使用各种高通量测序（NGS）技术，就不可能识别和富集具有新结构变体的 sRNA，这些技术允许检测植物中 sRNA 的总群体。根据这些高性能数据集对 sRNA 进行分类并预测其功能作用是一项具有挑战性的生物信息学任务，因为植物可以从多种生物合成途径中产生数百万种 sRNA。多年来，已经开发了许多计算工具来应对这一挑战。在这里，我们回顾了过去几年专门为植物 sRNA 开发的 35 多个工具，并探讨了它们的一些基本算法，用于执行与预测、识别、分类和量化植物样本中的单个 sRNA 以及可视化这些分析结果相关的任务。我们相信，对于想要分析其植物 sRNA 数据集但被可用工具数量所淹没的生物学家来说，这篇综述将是实用的，从而回答了如何为特定研究选择正确工具的基本问题。
