Department of Computer Science, Ben-Gurion University, Beer-Sheva 84105, Israel.
BMC Bioinformatics. 2010 May 13;11:249. doi: 10.1186/1471-2105-11-249.
MicroRNAs (miRNAs) are an abundant class of small noncoding RNAs (20-24 nts) that can affect gene expression by post-transcriptional regulation of mRNAs. They play important roles in several biological processes (e.g., development and cell cycle regulation). Numerous bioinformatics methods have been developed to identify the function of miRNAs by predicting their target mRNAs. Some viral organisms also encode miRNAs, a fact that contributes to the complex interactions between viruses and their hosts. A need arises to understand the functional relationship between viral and host miRNAs and their effect on viral and host genes. Our approach to meet this challenge is to identify modules where viral and host miRNAs cooperatively regulate host gene expression.
We present a method to identify groups of viral and host miRNAs that cooperate in post-transcriptional gene regulation, and their target genes that are involved in similar biological processes. We call these groups (genes and miRNAs of human and viral origin) - modules. The modules are found in a new two-stage procedure, which we call bi-targeting, and is presented in this paper. The stages are (i) a new and efficient target prediction, and (ii) a new method for clustering objects of three different data types. In this work we integrate multiple information sources, including miRNA-target binding information, miRNA expression profiles, and GO annotations. Our hypotheses and the methods have been tested on human and Epstein Barr virus (EBV) miRNAs and human genes, for which we found 34 modules. We provide supporting evidence from biological and medical literature for two of our modules. Our code and data are available at http://www.cs.bgu.ac.il/~vaksler/BiTargeting.htm
The presented algorithm, which makes use of diverse biological data, is demonstrated to be an efficient approach for finding bi-targeting modules of viral and human miRNAs. These modules can contribute to a better understanding of viral-host interactions and the role that miRNAs play in them.
MicroRNAs (miRNAs) 是一类丰富的小非编码 RNA（20-24 个核苷酸），可以通过 mRNA 的转录后调控来影响基因表达。它们在几个生物学过程（如发育和细胞周期调控）中发挥着重要作用。已经开发了许多生物信息学方法来通过预测其靶 mRNAs 来识别 miRNAs 的功能。一些病毒生物也编码 miRNAs，这一事实促成了病毒与其宿主之间的复杂相互作用。因此，需要了解病毒和宿主 miRNAs 之间的功能关系及其对病毒和宿主基因的影响。我们的方法是识别病毒和宿主 miRNAs 合作调节宿主基因表达的模块。
我们提出了一种识别病毒和宿主 miRNAs 合作在后转录基因调控中以及它们参与相似生物学过程的靶基因的方法。我们将这些（人类和病毒来源的基因和 miRNA）组称为模块。这些模块是在一个新的两阶段过程中发现的，我们称之为双靶向，并在本文中介绍。该阶段包括（i）新的和有效的靶标预测，以及（ii）一种新的聚类三种不同数据类型对象的方法。在这项工作中，我们整合了多个信息源，包括 miRNA-靶标结合信息、miRNA 表达谱和 GO 注释。我们的假设和方法已经在人类和 Epstein Barr 病毒 (EBV) miRNAs 和人类基因上进行了测试，发现了 34 个模块。我们为两个模块提供了来自生物和医学文献的支持证据。我们的代码和数据可在 http://www.cs.bgu.ac.il/~vaksler/BiTargeting.htm 获得。
所提出的算法利用了多种生物数据，被证明是一种寻找病毒和人类 miRNAs 的双靶向模块的有效方法。这些模块有助于更好地理解病毒-宿主相互作用以及 miRNAs 在其中所起的作用。
