Torkey Hanaa, Heath Lenwood S, ElHefnawi Mahmoud
* Department of Computer Science, Virginia Tech, Blacksburg, VA, 24061, Virginia.
† Informatics and Systems Department, National Research Centre, Cairo, Egypt.
J Bioinform Comput Biol. 2017 Aug;15(4):1750013. doi: 10.1142/S0219720017500135. Epub 2017 May 2.
MicroRNAs are known to play an essential role in gene regulation in plants and animals. The standard method for understanding microRNA-gene interactions is randomized controlled perturbation experiments. These experiments are costly and time consuming. Therefore, use of computational methods is essential. Currently, several computational methods have been developed to discover microRNA target genes. However, these methods have limitations based on the features that are used for prediction. The commonly used features are complementarity to the seed region of the microRNA, site accessibility, and evolutionary conservation. Unfortunately, not all microRNA target sites are conserved or adhere to exact seed complementary, and relying on site accessibility does not guarantee that the interaction exists. Moreover, the study of regulatory interactions composed of the same tissue expression data for microRNAs and mRNAs is necessary to understand the specificity of regulation and function. We developed MicroTarget to predict a microRNA-gene regulatory network using heterogeneous data sources, especially gene and microRNA expression data. First, MicroTarget employs expression data to learn a candidate target set for each microRNA. Then, it uses sequence data to provide evidence of direct interactions. MicroTarget scores and ranks the predicted targets based on a set of features. The predicted targets overlap with many of the experimentally validated ones. Our results indicate that using expression data in target prediction is more accurate in terms of specificity and sensitivity. Available at: https://bioinformatics.cs.vt.edu/~htorkey/microTarget .
已知微小RNA在植物和动物的基因调控中发挥着重要作用。理解微小RNA与基因相互作用的标准方法是随机对照扰动实验。这些实验成本高昂且耗时。因此，使用计算方法至关重要。目前，已经开发了几种计算方法来发现微小RNA靶基因。然而，这些方法基于用于预测的特征存在局限性。常用的特征包括与微小RNA种子区域的互补性、位点可及性和进化保守性。不幸的是，并非所有微小RNA靶位点都是保守的或严格遵循种子互补性，并且依赖位点可及性并不能保证相互作用的存在。此外，研究由微小RNA和mRNA的相同组织表达数据组成的调控相互作用对于理解调控和功能的特异性是必要的。我们开发了MicroTarget，利用异构数据源，特别是基因和微小RNA表达数据来预测微小RNA-基因调控网络。首先，MicroTarget利用表达数据为每个微小RNA学习一个候选靶集。然后，它使用序列数据来提供直接相互作用的证据。MicroTarget根据一组特征对预测的靶标进行评分和排序。预测的靶标与许多经实验验证的靶标重叠。我们的结果表明，在靶标预测中使用表达数据在特异性和敏感性方面更准确。可在以下网址获取：https://bioinformatics.cs.vt.edu/~htorkey/microTarget 。
