Bio-informatic trends for the determination of miRNA-target interactions in mammals

MicroRNAs (miRNAs) are small non-coding RNA molecules that regulate mRNAs through a sequence-specific mechanism. By virtue of their structure and mechanism of action, computational methods have been devised to investigate the encoding of miRNA genes and the targets of miRNA action. A variety of assumptions have predicated the implementation of these various computational solutions. Evolutionary sequence conservation, secondary structure, and folding energetics are some of the assumptions that have been used. The success of these different computational solutions has been evaluated for both elucidation of new miRNAs and deducing targets of miRNA action. While the focus is on search techniques for new miRNAs, we have compared the programs miRseeker, miRScan, PalGrade, ProMiR, and miRAlign as examples of implementation of these techniques. For these programs, a benchmark comparison between theoretical estimation and actual identification is possible. We have also compared the target prediction programs TargetScanS, PicTar, DIANA-microT, miRanda, and RNAhybrid. However, it is difficult to rigorously assess the benchmark performance of these programs due to the difficulty in confirming their theoretical predictions.     

Computational approaches for microRNA studies: a review

MicroRNAs (miRNAs) are one class of tiny, endogenous RNAs that can regulate messenger RNA (mRNA) expression by targeting homologous sequences in mRNAs. Their aberrant expressions have been observed in many cancers and several miRNAs have been convincingly shown to play important roles in carcinogenesis. Since the discovery of this small regulator, computational methods have been indispensable tools in miRNA gene finding and functional studies. In this review we first briefly outline the biological findings of miRNA genes, such as genomic feature, biogenesis, gene structure, and functional mechanism. We then discuss in detail the three main aspects of miRNA computational studies: miRNA gene finding, miRNA target prediction, and regulation of miRNA genes. Finally, we provide perspectives on some emerging issues, including combinatorial regulation by miRNAs and functional binding sites beyond the 3′-untranslated region (3′UTR) of target mRNAs. Available online resources for miRNA computational studies are also provided.     

Systematic identification of microRNA functions by combining target prediction and expression profiling

Target predictions and validations are major obstacles facing microRNA (miRNA) researchers. Animal miRNA target prediction is challenging because of limited miRNA sequence complementarity to the targets. In addition, only a small number of predicted targets have been experimentally validated and the miRNA mechanism is poorly understood. Here we present a novel algorithm for animal miRNA target prediction. The algorithm combines relevant parameters for miRNA target recognition and heuristically assigns different weights to these parameters according to their relative importance. A score calculation scheme is introduced to reflect the strength of each parameter. We also performed microarray time course experiments to identify downregulated genes due to miRNA overexpression. The computational target prediction is combined with the miRNA transfection experiment to systematically identify the gene targets of human miR-124. miR-124 overexpression led to a significant downregulation of many cell cycle related genes. This may be the result of direct suppression of a few cell growth inhibitors at the early stage of miRNA overexpression, and these targeted genes were continuously suppressed over a long period of time. Our high-throughput approach can be generalized to globally identify the targets and functions of other miRNAs.     

降解组测序技术在植物miRNA研究中的应用

目前, 利用芯片技术和miRNA测序可快速、准确地检测到物种中所含有的miRNA。随着越来越多的miRNA被发现, miRNA靶基因的确定已成为研究miRNA生物学功能的关键。传统的miRNA靶基因的寻找主要依赖生物信息学预测、AGO蛋白免疫共沉淀和荧光素酶法等。随着高通量测序技术的持续革新, 出现了一种新的miRNA靶基因的检测方法, 即降解组测序(degradome sequencing)法, 该方法拥有高通量测序技术、生物信息学分析和RACE验证三者的优势, 并已成功应用于拟南芥(Arabidopsis thaliana)、水稻(Oryza sativa)和小立碗藓(Physcomitrella patens)等模式植物miRNA靶基因的检测。基于已发表的相关文献和联川生物降解组测序平台, 该文对降解组测序技术应用于植物miRNA靶基因的研究进展及其实验原理进行了综述, 同时对运用该技术可进行的更深入研究进行了讨论。     

Computational identification of novel microRNAs and targets in Brassica napus

MicroRNAs (miRNAs) are a newly discovered class of non-protein-coding small RNAs with roughly 22 nucleotide-long. Increasing evidence has shown that miRNAs play multiple roles in biological processes, including development, cell proliferation and apoptosis and stress responses. In this research, several approaches were combined to make computational prediction of potential miRNAs and their targets in Brassica napus. We used previously known miRNAs from Arabidopsis, rice and other plant species against both expressed sequence tags (EST) and genomic survey sequence (GSS) databases to search for potential miRNAs in B. napus. A total of 21 potential miRNAs were detected following a range of strict filtering criteria. Using these potential miRNA sequences, we could further blast the mRNA database and found 67 potential targets in this species. According to the mRNA target information provided by NCBI (http://www.ncbi.nlm.nih.gov/), most of the target mRNAs appeared to be involved in plant growth, development and stress responses. To validate the prediction of miRNAs in B. napus, we performed a RT-PCR based assay of mature miRNA expression. Five miRNAs were identified in response to auxin, cadmium stress and phosphate starvation. So far, little is known about experimental or computational identification of miRNA in B. napus species. To improve efficiency for blast search, we developed an implementation (miRNAassist) that can identify homologs of miRNAs and their targets, with high sensitivity and specificity. The program is allowed to be run on Windows Operation System platform. miRNAassist is freely available if required.     

计算识别microRNA及其靶基因

小RNA的发现为基因调控系统研究提供了新的方向。在多数物种中已经发现了大量的小RNA。这一领域已经成为了近来研究的热点,在研究起始阶段,计算学方法已经成为实验研究中不可或缺的工具,许多发现是由生物学实验与计算学方法共同合作来完成的。在这篇综述中,我们总结了前人关于小RNA及其靶基因识别的理论知识。最后,讨论了关于预测小RNA及其靶基因的计算学方法和相关软件。     

HomoTarget: A new algorithm for prediction of microRNA targets in Homo sapiens

MiRNAs play an essential role in the networks of gene regulation by inhibiting the translation of target mRNAs. Several computational approaches have been proposed for the prediction of miRNA target-genes. Reports reveal a large fraction of under-predicted or falsely predicted target genes. Thus, there is an imperative need to develop a computational method by which the target mRNAs of existing miRNAs can be correctly identified. In this study, combined pattern recognition neural network (PRNN) and principle component analysis (PCA) architecture has been proposed in order to model the complicated relationship between miRNAs and their target mRNAs in humans. The results of several types of intelligent classifiers and our proposed model were compared, showing that our algorithm outperformed them with higher sensitivity and specificity. Using the recent release of the mirBase database to find potential targets of miRNAs, this model incorporated twelve structural, thermodynamic and positional features of miRNA:mRNA binding sites to select target candidates.     

MCLPMDA: A novel method for miRNA‐disease association prediction based on matrix completion and label propagation

MiRNAs are a class of small non‐coding RNAs that are involved in the development and progression of various complex diseases. Great efforts have been made to discover potential associations between miRNAs and diseases recently. As experimental methods are in general expensive and time‐consuming, a large number of computational models have been developed to effectively predict reliable disease‐related miRNAs. However, the inherent noise and incompleteness in the existing biological datasets have inevitably limited the prediction accuracy of current computational models. To solve this issue, in this paper, we propose a novel method for miRNA‐disease association prediction based on matrix completion and label propagation. Specifically, our method first reconstructs a new miRNA/disease similarity matrix by matrix completion algorithm based on known experimentally verified miRNA‐disease associations and then utilizes the label propagation algorithm to reliably predict disease‐related miRNAs. As a result, MCLPMDA achieved comparable performance under different evaluation metrics and was capable of discovering greater number of true miRNA‐disease associations. Moreover, case study conducted on Breast Neoplasms further confirmed the prediction reliability of the proposed method. Taken together, the experimental results clearly demonstrated that MCLPMDA can serve as an effective and reliable tool for miRNA‐disease association prediction.