School of Biological Sciences, Seoul National University, Seoul 08826, Korea.
Mol Cells. 2023 Jan 31;46(1):21-32. doi: 10.14348/molcells.2023.2157. Epub 2023 Jan 4.
MicroRNAs (miRNAs) play cardinal roles in regulating biological pathways and processes, resulting in significant physiological effects. To understand the complex regulatory network of miRNAs, previous studies have utilized massivescale datasets of miRNA targeting and attempted to computationally predict the functional targets of miRNAs. Many miRNA target prediction tools have been developed and are widely used by scientists from various fields of biology and medicine. Most of these tools consider seed pairing between miRNAs and their mRNA targets and additionally consider other determinants to improve prediction accuracy. However, these tools exhibit limited prediction accuracy and high false positive rates. The utilization of additional determinants, such as RNA modifications and RNA-binding protein binding sites, may further improve miRNA target prediction. In this review, we discuss the determinants of functional miRNA targeting that are currently used in miRNA target prediction and the potentially predictive but unappreciated determinants that may improve prediction accuracy.
微小 RNA(miRNA)在调节生物途径和过程中起着重要作用,产生显著的生理效应。为了理解 miRNA 的复杂调控网络,之前的研究利用了大规模的 miRNA 靶向数据集,并尝试计算预测 miRNA 的功能靶标。已经开发出许多 miRNA 靶标预测工具,并被来自生物学和医学各个领域的科学家广泛使用。这些工具中的大多数都考虑了 miRNA 与其 mRNA 靶标的种子配对,并且还考虑了其他决定因素来提高预测准确性。然而,这些工具的预测准确性有限,假阳性率较高。利用附加决定因素,如 RNA 修饰和 RNA 结合蛋白结合位点,可能进一步提高 miRNA 靶标预测的准确性。在这篇综述中,我们讨论了当前用于 miRNA 靶标预测的功能性 miRNA 靶向的决定因素,以及可能提高预测准确性的潜在预测性但未被充分认识的决定因素。