Naifang Su, Minping Qian, Minghua Deng
LMAM, School of Mathematical Sciences, Peking University, Beijing 100871, P.R. China ; Beijing International Center for Mathematical Research, Peking University, Beijing 100871, P.R. China.
Curr Bioinform. 2013 Feb;8(1):37-45. doi: 10.2174/1574893611308010008.
Gene regulation is a key factor in gaining a full understanding of molecular biology. microRNA (miRNA), a novel class of non-coding RNA, has recently been found to be one crucial class of post-transactional regulators, and play important roles in cancer. One essential step to understand the regulatory effect of miRNAs is the reliable prediction of their target mRNAs. Typically, the predictions are solely based on the sequence information, which unavoidably have high false detection rates. Recently, some novel approaches are developed to predict miRNA targets by integrating the typical algorithm with the paired expression profiles of miRNA and mRNA. Here we review and discuss these integrative approaches and propose a new algorithm called HCTarget. Applying HCtarget to the expression data in multiple myeloma, we predict target genes for ten specific miRNAs. The experimental verification and a loss of function study validate our predictions. Therefore, the integrative approach is a reliable and effective way to predict miRNA targets, and could improve our comprehensive understanding of gene regulation.
基因调控是全面理解分子生物学的关键因素。微小RNA（miRNA）是一类新型非编码RNA，最近被发现是一类关键的转录后调节因子，在癌症中发挥重要作用。理解miRNA调控作用的一个重要步骤是可靠地预测其靶mRNA。通常，预测仅基于序列信息，这不可避免地具有较高的错误检测率。最近，一些新方法被开发出来，通过将典型算法与miRNA和mRNA的配对表达谱相结合来预测miRNA靶标。在此，我们回顾并讨论这些整合方法，并提出一种名为HCTarget的新算法。将HCTarget应用于多发性骨髓瘤的表达数据，我们预测了十种特定miRNA的靶基因。实验验证和功能缺失研究证实了我们的预测。因此，整合方法是预测miRNA靶标的可靠有效方法，能够增进我们对基因调控的全面理解。
