Brief Bioinform. 2019 Jul 19;20(4):1193-1204. doi: 10.1093/bib/bbx137.
Posttranscriptional crosstalk and communication between RNAs yield large regulatory competing endogenous RNA (ceRNA) networks via shared microRNAs (miRNAs), as well as miRNA synergistic networks. The ceRNA crosstalk represents a novel layer of gene regulation that controls both physiological and pathological processes such as development and complex diseases. The rapidly expanding catalogue of ceRNA regulation has provided evidence for exploitation as a general model to predict the ceRNAs in silico. In this article, we first reviewed the current progress of RNA-RNA crosstalk in human complex diseases. Then, the widely used computational methods for modeling ceRNA-ceRNA interaction networks are further summarized into five types: two types of global ceRNA regulation prediction methods and three types of context-specific prediction methods, which are based on miRNA-messenger RNA regulation alone, or by integrating heterogeneous data, respectively. To provide guidance in the computational prediction of ceRNA-ceRNA interactions, we finally performed a comparative study of different combinations of miRNA-target methods as well as five types of ceRNA identification methods by using literature-curated ceRNA regulation and gene perturbation. The results revealed that integration of different miRNA-target prediction methods and context-specific miRNA/gene expression profiles increased the performance for identifying ceRNA regulation. Moreover, different computational methods were complementary in identifying ceRNA regulation and captured different functional parts of similar pathways. We believe that the application of these computational techniques provides valuable functional insights into ceRNA regulation and is a crucial step for informing subsequent functional validation studies.
RNA 间转录后串扰和交流通过共享 microRNAs（miRNAs）以及 miRNA 协同网络产生大型调控竞争内源性 RNA（ceRNA）网络。ceRNA 串扰代表了一种新的基因调控层，可控制生理和病理过程，如发育和复杂疾病。ceRNA 调控的快速扩展目录为作为一种普遍模型来预测计算机中的 ceRNA 提供了证据。在本文中，我们首先回顾了 RNA-RNA 串扰在人类复杂疾病中的当前进展。然后，进一步总结了广泛用于构建 ceRNA-ceRNA 相互作用网络的计算方法，分为五种类型：两种全局 ceRNA 调控预测方法和三种基于 miRNA-信使 RNA 调控或分别整合异质数据的特定于上下文的预测方法。为了在 ceRNA-ceRNA 相互作用的计算预测中提供指导，我们最后通过使用文献编纂的 ceRNA 调控和基因扰动，对不同 miRNA 靶向方法组合以及五种 ceRNA 识别方法进行了比较研究。结果表明，整合不同的 miRNA 靶向预测方法和特定于上下文的 miRNA/基因表达谱可提高识别 ceRNA 调控的性能。此外，不同的计算方法在识别 ceRNA 调控方面具有互补性，并捕获了相似途径的不同功能部分。我们相信这些计算技术的应用为 ceRNA 调控提供了有价值的功能见解，是告知后续功能验证研究的关键步骤。
