Department of Bioscience and Bioinformatics, Kyushu Institute of Technology, 680-4 Kawazu, Iizuka, Fukuoka 820-8502, Japan.
Tulane Center for Biomedical Informatics and Genomics, Division of Biomedical Informatics and Genomics, John W. Deming Department of Medicine, School of Medicine, Tulane University, New Orleans, LA 70112, USA.
Curr Med Chem. 2022;29(5):865-880. doi: 10.2174/0929867328666210804090224.
MicroRNAs (miRNAs) are central players that regulate the post-transcriptional processes of gene expression. Binding of miRNAs to target mRNAs can repress their translation by inducing the degradation or by inhibiting the translation of the target mRNAs. Highthroughput experimental approaches for miRNA target identification are costly and timeconsuming, depending on various factors. It is vitally important to develop bioinformatics methods for accurately predicting miRNA targets. With the increase of RNA sequences in the post-genomic era, bioinformatics methods are being developed for miRNA studies especially for miRNA target prediction. This review summarizes the current development of state-of-the-art bioinformatics tools for miRNA target prediction, points out the progress and limitations of the available miRNA databases, and their working principles. Finally, we discuss the caveat and perspectives of the next-generation algorithms for the prediction of miRNA targets.
微小 RNA（miRNA）是调节基因表达转录后过程的关键调控因子。miRNA 与靶 mRNA 的结合可以通过诱导靶 mRNA 的降解或抑制其翻译来抑制靶 mRNA 的翻译。miRNA 靶标识别的高通量实验方法成本高且耗时，取决于各种因素。因此，开发用于准确预测 miRNA 靶标的生物信息学方法至关重要。在后基因组时代，随着 RNA 序列的增加，生物信息学方法也被用于 miRNA 研究，特别是 miRNA 靶标预测。本综述总结了 miRNA 靶标预测的最新生物信息学工具的最新进展，指出了现有 miRNA 数据库的优缺点及其工作原理。最后，我们讨论了用于 miRNA 靶标预测的下一代算法的注意事项和前景。
