Zhao Yuming, Wang Fang, Chen Su, Wan Jun, Wang Guohua
State Key Laboratory of Tree Genetics and Breeding, Northeast Forestry University, Harbin, Heilongjiang, China.
Information and Computer Engineering College, Northeast Forestry University, Harbin, Heilongjiang, China.
Biomed Res Int. 2017;2017:7049406. doi: 10.1155/2017/7049406. Epub 2017 Jun 5.
MicroRNAs (miRNAs) are short (~22 nucleotides) noncoding RNAs and disseminated throughout the genome, either in the intergenic regions or in the intronic sequences of protein-coding genes. MiRNAs have been proved to play important roles in regulating gene expression. Hence, understanding the transcriptional mechanism of miRNA genes is a very critical step to uncover the whole regulatory network. A number of miRNA promoter prediction models have been proposed in the past decade. This review summarized several most popular miRNA promoter prediction models which used genome sequence features, or other features, for example, histone markers, RNA Pol II binding sites, and nucleosome-free regions, achieved by high-throughput sequencing data. Some databases were described as resources for miRNA promoter information. We then performed comprehensive discussion on prediction and identification of transcription factor mediated microRNA regulatory networks.
微小RNA（miRNA）是短链（约22个核苷酸）非编码RNA，分布于整个基因组中，存在于基因间区域或蛋白质编码基因的内含子序列中。已证明miRNA在调节基因表达中发挥重要作用。因此，了解miRNA基因的转录机制是揭示整个调控网络的关键步骤。在过去十年中，已经提出了许多miRNA启动子预测模型。本综述总结了几种最流行的miRNA启动子预测模型，这些模型利用基因组序列特征或其他特征（例如通过高通量测序数据获得的组蛋白标记、RNA聚合酶II结合位点和无核小体区域）进行预测。还介绍了一些作为miRNA启动子信息资源的数据库。然后，我们对转录因子介导的微小RNA调控网络的预测和识别进行了全面讨论。
