Zhang Weijia, Le Thuc Duy, Liu Lin, Zhou Zhi-Hua, Li Jiuyong
School of Information Technology and Mathematical Sciences, University of South Australia, Adelaide, South Australia, Australia.
National Key Labotorary, Nanjing University, Nanjing, Jiangsu, China.
PLoS One. 2016 Apr 11;11(4):e0152860. doi: 10.1371/journal.pone.0152860. eCollection 2016.
microRNAs (miRNAs) play crucial roles in post-transcriptional gene regulation of both plants and mammals, and dysfunctions of miRNAs are often associated with tumorigenesis and development through the effects on their target messenger RNAs (mRNAs). Identifying miRNA functions is critical for understanding cancer mechanisms and determining the efficacy of drugs. Computational methods analyzing high-throughput data offer great assistance in understanding the diverse and complex relationships between miRNAs and mRNAs. However, most of the existing methods do not fully utilise the available knowledge in biology to reduce the uncertainty in the modeling process. Therefore it is desirable to develop a method that can seamlessly integrate existing biological knowledge and high-throughput data into the process of discovering miRNA regulation mechanisms.
In this article we present an integrative framework, CIDER (Causal miRNA target Discovery with Expression profile and Regulatory knowledge), to predict miRNA targets. CIDER is able to utilise a variety of gene regulation knowledge, including transcriptional and post-transcriptional knowledge, and to exploit gene expression data for the discovery of miRNA-mRNA regulatory relationships. The benefits of our framework is demonstrated by both simulation study and the analysis of the epithelial-to-mesenchymal transition (EMT) and the breast cancer (BRCA) datasets. Our results reveal that even a limited amount of either Transcription Factor (TF)-miRNA or miRNA-mRNA regulatory knowledge improves the performance of miRNA target prediction, and the combination of the two types of knowledge enhances the improvement further. Another useful property of the framework is that its performance increases monotonically with the increase of regulatory knowledge.
微小RNA（miRNA）在植物和哺乳动物的转录后基因调控中发挥着关键作用，miRNA功能失调通常通过影响其靶信使核糖核酸（mRNA）与肿瘤发生和发展相关。识别miRNA功能对于理解癌症机制和确定药物疗效至关重要。分析高通量数据的计算方法在理解miRNA与mRNA之间多样而复杂的关系方面提供了很大帮助。然而，大多数现有方法并未充分利用生物学中的现有知识来减少建模过程中的不确定性。因此，需要开发一种能够将现有生物学知识和高通量数据无缝整合到发现miRNA调控机制过程中的方法。
在本文中，我们提出了一个综合框架CIDER（基于表达谱和调控知识的因果miRNA靶标发现）来预测miRNA靶标。CIDER能够利用多种基因调控知识，包括转录和转录后知识，并利用基因表达数据来发现miRNA - mRNA调控关系。模拟研究以及对上皮 - 间充质转化（EMT）和乳腺癌（BRCA）数据集的分析均证明了我们框架的优势。我们的结果表明，即使是有限数量的转录因子（TF） - miRNA或miRNA - mRNA调控知识也能提高miRNA靶标预测的性能，并且这两种知识的结合能进一步增强这种提升效果。该框架的另一个有用特性是其性能随着调控知识的增加而单调提高。
