Huang Kai-Yao, Lee Tzong-Yi, Teng Yu-Chuan, Chang Tzu-Hao
BMC Bioinformatics. 2015;16 Suppl 1(Suppl 1):S9. doi: 10.1186/1471-2105-16-S1-S9. Epub 2015 Jan 21.
microRNAs (miRNAs) play a vital role in development, oncogenesis, and apoptosis by binding to mRNAs to regulate the posttranscriptional level of coding genes in mammals, plants, and insects. Recent studies have demonstrated that the expression of viral miRNAs is associated with the ability of the virus to infect a host. Identifying potential viral miRNAs from experimental sequence data is valuable for deciphering virus-host interactions. Thus far, a specific predictive model for viral miRNA identification has yet to be developed.
Here, we present ViralmiR for identifying viral miRNA precursors on the basis of sequencing and structural information. We collected 263 experimentally validated miRNA precursors (pre-miRNAs) from 26 virus species and generated sequencing fragments from virus and human genomes as the negative dataset. Support vector machine and random forest models were established using 54 features from RNA sequences and secondary structural information. The results show that ViralmiR achieved a balanced accuracy higher than 83%, which is superior to that of previously developed tools for identifying pre-miRNAs.
The easy-to-use ViralmiR web interface has been provided as a helpful resource for researchers to use in analyzing and deciphering virus-host interactions. The web interface of ViralmiR can be accessed at http://csb.cse.yzu.edu.tw/viralmir/.
微小RNA（miRNA）通过与信使核糖核酸（mRNA）结合，在哺乳动物、植物和昆虫中调控编码基因的转录后水平，在发育、肿瘤发生和细胞凋亡中发挥着至关重要的作用。最近的研究表明，病毒miRNA的表达与病毒感染宿主的能力有关。从实验序列数据中识别潜在的病毒miRNA对于解读病毒与宿主的相互作用具有重要价值。到目前为止，尚未开发出用于识别病毒miRNA的特定预测模型。
在此，我们提出了ViralmiR，用于基于测序和结构信息识别病毒miRNA前体。我们从26种病毒中收集了263个经过实验验证的miRNA前体（pre-miRNA），并从病毒和人类基因组中生成测序片段作为阴性数据集。利用RNA序列和二级结构信息中的54个特征建立了支持向量机和随机森林模型。结果表明，ViralmiR实现了高于83%的平衡准确率，优于先前开发的用于识别pre-miRNA的工具。
已提供易于使用的ViralmiR网络界面，作为研究人员分析和解读病毒与宿主相互作用的有用资源。可通过http://csb.cse.yzu.edu.tw/viralmir/访问ViralmiR的网络界面。
