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植物 miRNA 靶标计算分析:现状与挑战。

Computational analysis of miRNA targets in plants: current status and challenges.

机构信息

Plant Biology Division, The Samuel Roberts Noble Foundation, Ardmore, OK 73401, USA.

出版信息

Brief Bioinform. 2011 Mar;12(2):115-21. doi: 10.1093/bib/bbq065. Epub 2010 Sep 21.

Abstract

Plant microRNAs (miRNA) target recognition mechanism was once thought to be simple and straightforward, i.e. through perfect reverse complementary matching; therefore, very few target prediction tools and algorithms were developed for plants as compared to those for animals. However, the discovery of transcription suppression and the more recent observation of widespread translational regulation by miRNAs highlight the enormous diversity and complexity of gene regulation in plant systems. This, in turn, necessitates the need for advanced computational tools/algorithms for comprehensive miRNA target analysis to help understand miRNA regulatory mechanisms. Yet, advanced/comprehensive plant miRNA target analysis tools are still lacking despite the desirability and importance of such tools, especially the ability of predicting translational inhibition and integrating transcriptome data. This review focuses on recent progress in plant miRNA target recognition mechanism, principles of target prediction based on these understandings, comparison of current prediction tools and algorithms for plant miRNA target analysis and the outlook for future directions in the development of plant miRNA target tools and algorithms.

摘要

植物 microRNAs (miRNA) 靶标识别机制曾经被认为是简单直接的,即通过完全的反向互补匹配;因此,与动物相比,开发的植物靶标预测工具和算法非常少。然而,转录抑制的发现以及最近观察到的 miRNA 对翻译的广泛调控,突出了植物系统中基因调控的巨大多样性和复杂性。这反过来又需要先进的计算工具/算法来进行全面的 miRNA 靶标分析,以帮助理解 miRNA 调控机制。然而,尽管这些工具的可取性和重要性很高,但仍然缺乏先进/全面的植物 miRNA 靶标分析工具,特别是预测翻译抑制和整合转录组数据的能力。这篇综述重点介绍了植物 miRNA 靶标识别机制的最新进展,基于这些理解的靶标预测原则,以及当前植物 miRNA 靶标分析预测工具和算法的比较,以及植物 miRNA 靶标工具和算法未来发展方向的展望。

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