CEIT, University of Navarra, San Sebastian, Spain.
Brief Bioinform. 2013 May;14(3):263-78. doi: 10.1093/bib/bbs028. Epub 2012 Jun 12.
miRNAs are small RNA molecules ('22 nt) that interact with their target mRNAs inhibiting translation or/and cleavaging the target mRNA. This interaction is guided by sequence complentarity and results in the reduction of mRNA and/or protein levels. miRNAs are involved in key biological processes and different diseases. Therefore, deciphering miRNA targets is crucial for diagnostics and therapeutics. However, miRNA regulatory mechanisms are complex and there is still no high-throughput and low-cost miRNA target screening technique. In recent years, several computational methods based on sequence complementarity of the miRNA and the mRNAs have been developed. However, the predicted interactions using these computational methods are inconsistent and the expected false positive rates are still large. Recently, it has been proposed to use the expression values of miRNAs and mRNAs (and/or proteins) to refine the results of sequence-based putative targets for a particular experiment. These methods have shown to be effective identifying the most prominent interactions from the databases of putative targets. Here, we review these methods that combine both expression and sequence-based putative targets to predict miRNA targets.
miRNAs 是一种小 RNA 分子（“22nt”），可以与靶 mRNA 相互作用，抑制翻译或/和切割靶 mRNA。这种相互作用受序列互补性的指导，导致 mRNA 和/或蛋白质水平降低。miRNAs 参与关键的生物学过程和不同的疾病。因此，破译 miRNA 靶标对于诊断和治疗至关重要。然而，miRNA 的调控机制很复杂，仍然没有高通量、低成本的 miRNA 靶标筛选技术。近年来，已经开发了几种基于 miRNA 和 mRNAs 序列互补性的计算方法。然而，这些计算方法预测的相互作用并不一致，预期的假阳性率仍然很大。最近，有人提出利用 miRNA 和 mRNAs（和/或蛋白质）的表达值来细化特定实验中基于序列的假定靶标的结果。这些方法已被证明能够有效地从假定靶标数据库中识别出最突出的相互作用。在这里，我们综述了这些将表达和基于序列的假定靶标相结合来预测 miRNA 靶标的方法。
