Fiannaca Antonino, Rosa Massimo La, Paglia Laura La, Rizzo Riccardo, Urso Alfonso
National Research Council of Italy, ICAR-CNR, via Ugo La Malfa 153, Palermo, 90146, Italy.
BMC Bioinformatics. 2016 Sep 22;17(Suppl 11):321. doi: 10.1186/s12859-016-1171-x.
MicroRNAs (miRNAs) are small non-coding RNA sequences with regulatory functions to post-transcriptional level for several biological processes, such as cell disease progression and metastasis. MiRNAs interact with target messenger RNA (mRNA) genes by base pairing. Experimental identification of miRNA target is one of the major challenges in cancer biology because miRNAs can act as tumour suppressors or oncogenes by targeting different type of targets. The use of machine learning methods for the prediction of the target genes is considered a valid support to investigate miRNA functions and to guide related wet-lab experiments. In this paper we propose the miRNA Target Interaction Predictor (miRNATIP) algorithm, a Self-Organizing Map (SOM) based method for the miRNA target prediction. SOM is trained with the seed region of the miRNA sequences and then the mRNA sequences are projected into the SOM lattice in order to find putative interactions with miRNAs. These interactions will be filtered considering the remaining part of the miRNA sequences and estimating the free-energy necessary for duplex stability.
We tested the proposed method by predicting the miRNA target interactions of both the Homo sapiens and the Caenorhbditis elegans species; then, taking into account validated target (positive) and non-target (negative) interactions, we compared our results with other target predictors, namely miRanda, PITA, PicTar, mirSOM, TargetScan and DIANA-microT, in terms of the most used statistical measures. We demonstrate that our method produces the greatest number of predictions with respect to the other ones, exhibiting good results for both species, reaching the for example the highest percentage of sensitivity of 31 and 30.5 %, respectively for Homo sapiens and for C. elegans. All the predicted interaction are freely available at the following url: http://tblab.pa.icar.cnr.it/public/miRNATIP/ .
Results state miRNATIP outperforms or is comparable to the other six state-of-the-art methods, in terms of validated target and non-target interactions, respectively.
微小RNA（miRNA）是具有调控功能的小非编码RNA序列，可在转录后水平调控多种生物学过程，如细胞疾病进展和转移。miRNA通过碱基配对与靶信使RNA（mRNA）基因相互作用。miRNA靶标的实验鉴定是癌症生物学中的主要挑战之一，因为miRNA可通过靶向不同类型的靶标发挥肿瘤抑制因子或致癌基因的作用。使用机器学习方法预测靶基因被认为是研究miRNA功能和指导相关湿实验室实验的有效支持。在本文中，我们提出了miRNA靶标相互作用预测器（miRNATIP）算法，这是一种基于自组织映射（SOM）的miRNA靶标预测方法。SOM使用miRNA序列的种子区域进行训练，然后将mRNA序列投影到SOM晶格中，以寻找与miRNA的假定相互作用。这些相互作用将根据miRNA序列的其余部分进行筛选，并估计双链稳定性所需的自由能。
我们通过预测人类和秀丽隐杆线虫物种的miRNA靶标相互作用来测试所提出的方法；然后，考虑到经过验证的靶标（阳性）和非靶标（阴性）相互作用，我们根据最常用的统计量将我们的结果与其他靶标预测器（即miRanda、PITA、PicTar、mirSOM、TargetScan和DIANA-microT）进行了比较。我们证明，相对于其他方法，我们的方法产生的预测数量最多，对两个物种都显示出良好的结果，例如，人类和秀丽隐杆线虫的灵敏度分别达到最高百分比31%和30.5%。所有预测的相互作用可在以下网址免费获取：http://tblab.pa.icar.cnr.it/public/miRNATIP/ 。
结果表明，就经过验证的靶标和非靶标相互作用而言，miRNATIP分别优于或与其他六种最先进的方法相当。
