miRDB: a microRNA target prediction and functional annotation database with a wiki interface

MicroRNAs (miRNAs) are short noncoding RNAs that are involved in the regulation of thousands of gene targets. Recent studies indicate that miRNAs are likely to be master regulators of many important biological processes. Due to their functional importance, miRNAs are under intense study at present, and many studies have been published in recent years on miRNA functional characterization. The rapid accumulation of miRNA knowledge makes it challenging to properly organize and present miRNA function data. Although several miRNA functional databases have been developed recently, this remains a major bioinformatics challenge to miRNA research community. Here, we describe a new online database system, miRDB, on miRNA target prediction and functional annotation. Flexible web search interface was developed for the retrieval of target prediction results, which were generated with a new bioinformatics algorithm we developed recently. Unlike most other miRNA databases, miRNA functional annotations in miRDB are presented with a primary focus on mature miRNAs, which are the functional carriers of miRNA-mediated gene expression regulation. In addition, a wiki editing interface was established to allow anyone with Internet access to make contributions on miRNA functional annotation. This is a new attempt to develop an interactive community-annotated miRNA functional catalog. All data stored in miRDB are freely accessible at http://mirdb.org.     

MicroRNA target prediction based on second-order Hidden Markov Model

MicroRNAs are one class of small single-stranded RNA of about 22 nt serving as important negative gene regulators. In animals, miRNAs mainly repress protein translation by binding itself to the 3′ UTR regions of mRNAs with imperfect complementary pairing. Although bioinformatics investigations have resulted in a number of target prediction tools, all of these have a common shortcoming—a high false positive rate. Therefore, it is important to further filter the predicted targets. In this paper, based on miRNA:target duplex, we construct a second-order Hidden Markov Model, implement Baum-Welch training algorithm and apply this model to further process predicted targets. The model trains the classifier by 244 positive and 49 negative miRNA:target interaction pairs and achieves a sensitivity of 72.54%, specificity of 55.10% and accuracy of 69.62% by 10-fold cross-validation experiments. In order to further verify the applicability of the algorithm, previously collected datasets, including 195 positive and 38 negative, are chosen to test it, with consistent results. We believe that our method will provide some guidance for experimental biologists, especially in choosing miRNA targets for validation.     

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.     

miRNA与其靶mRNA的相互作用：绑定位点的质量与数量特征的整合计算分析

miRNAs通过完全或不完全的碱基互补绑定到信使RNA(mRNA)上,通过抑制翻译或者直接导致mRNA降解的方式来调节靶基因的表达．为了研究miRNAs在转录水平上面的调控作用,两种人类基因组中组织特异的miRNAs(miR-1和miR-124)被转染到HeLa细胞中,微阵列(microarray)分析转染前后细胞中各基因mRNA表达水平变化情况的结果表明：动物基因组中靶基因与miRNAs不完全的碱基互补也会导致mRNA的直接降解．通过分析实验得到的mRNA表达水平变化数据,发现这相同miRNA的不同靶基因mRNA表达水平的下调倍数有着明显的差别,推测这些靶基因mRNA序列本身存在某些影响其受调节程度的因素．为此,提取和分析这些靶基因mRNA的序列特征,通过对这些序列特征与mRNA表达水平下调数据进行统计相关分析,最终发现,miRNA靶基因受调节的程度与以下几个因素相关联：mRNA序列中miRNA靶位点的个数,靶位点与miRNA序列碱基互补的程度,以及绑定后形成二级结构的稳定程度(即最低自由能的大小)．在此基础上,初步建立起一个多因子作用下的miRNA 靶基因mRNA表达水平下调程度模型,分析表明：该模型在一定程度上可以反映了部分序列特征对于miRNA靶基因mRNA表达水平下调程度的影响．     

利用深度测序技术检测玉米根系和叶片中已知的microRNAs

microRNA(miRNA)是一类具有20～24nt核苷酸长度的非蛋白质编码的内源小分子RNA,它在植物生长发育和逆境胁迫响应等过程中发挥着重要作用。文章利用基于Illumina/Solexa原理的小分子RNA深度测序技术,结合生物信息学的方法对玉米根系和叶片中已知miRNA的类型、丰度及靶基因进行了分析。研究发现,在根系中共检测到92个已知的miRNA,分别属于18个miRNA家族,其表达丰度在1～105943之间;在叶片中,共发现86个已知的miRNA,分别属于17个miRNA家族,其表达丰度在1～85973之间。靶基因预测结果表明,根系中的18个miRNA家族共靶向54个蛋白,进一步的功能预测发现,这些基因涉及了转录调控、物质能量代谢、电子传递、胁迫响应和信号转导等过程。以上研究结果表明,就已知的miRNA而言,无论是miRNA的类型还是表达丰度,在玉米根系和叶片中都存在较大差异。     

小RNA(MicroRNA)研究方法

小RNA (microRNA)是一类新发现的长度约为21～25个核苷酸的RNA,它在转录后水平调节靶基因表达.已有研究表明,小RNA在发育、细胞增殖、凋亡、脂类代谢、激素分泌及肿瘤发生等多种生理和病理过程中发挥重要作用.针对小RNA的研究方法主要包括两大类:一是以传统实验技术方法为基础建立起来的小RNA特有的技术方法,二是已成熟应用的生物信息学技术.前者侧重于小RNA表达的检测和功能机制的阐明,后者则包括新小RNA基因及小RNA靶基因的预测.两者相辅相成,互为补充,为深入地研究这类分子的功能和分子机制提供了大量功能线索,及确凿的实验证据.     

Inference of miRNA targets using evolutionary conservation and pathway analysis

Background

MicroRNAs have emerged as important regulatory genes in a variety of cellular processes and, in recent years, hundreds of such genes have been discovered in animals. In contrast, functional annotations are available only for a very small fraction of these miRNAs, and even in these cases only partially.

Results

We developed a general Bayesian method for the inference of miRNA target sites, in which, for each miRNA, we explicitly model the evolution of orthologous target sites in a set of related species. Using this method we predict target sites for all known miRNAs in flies, worms, fish, and mammals. By comparing our predictions in fly with a reference set of experimentally tested miRNA-mRNA interactions we show that our general method performs at least as well as the most accurate methods available to date, including ones specifically tailored for target prediction in fly. An important novel feature of our model is that it explicitly infers the phylogenetic distribution of functional target sites, independently for each miRNA. This allows us to infer species-specific and clade-specific miRNA targeting. We also show that, in long human 3' UTRs, miRNA target sites occur preferentially near the start and near the end of the 3' UTR. To characterize miRNA function beyond the predicted lists of targets we further present a method to infer significant associations between the sets of targets predicted for individual miRNAs and specific biochemical pathways, in particular those of the KEGG pathway database. We show that this approach retrieves several known functional miRNA-mRNA associations, and predicts novel functions for known miRNAs in cell growth and in development.

Conclusion

We have presented a Bayesian target prediction algorithm without any tunable parameters, that can be applied to sequences from any clade of species. The algorithm automatically infers the phylogenetic distribution of functional sites for each miRNA, and assigns a posterior probability to each putative target site. The results presented here indicate that our general method achieves very good performance in predicting miRNA target sites, providing at the same time insights into the evolution of target sites for individual miRNAs. Moreover, by combining our predictions with pathway analysis, we propose functions of specific miRNAs in nervous system development, inter-cellular communication and cell growth. The complete target site predictions as well as the miRNA/pathway associations are accessible on the ElMMo web server.     

生物信息学在植物miRNA研究中的应用

植物miRNA的研究已经从小规模实验向大规模计算分析方向发展,生物信息学的应用成为当前植物miRNA研究的热点问题。本文回顾了最近几年生物信息学在植物miRNA研究中取得的最新进展,简要介绍了植物miRNA的形成及其作用方式,重点对植物miRNA的计算识别、靶基因预测、启动子分析方法进行了讨论,并对相关的数据库资源进行了总结,最后展望了该领域研究的发展方向,将为植物miRNA的计算研究提供理论指导。     

Adaptive evolution of testis-specific,recently evolved,clustered miRNAs in Drosophila

The propensity of animal miRNAs to regulate targets bearing modest complementarity, most notably via pairing with miRNA positions ∼2–8 (the “seed”), is believed to drive major aspects of miRNA evolution. First, minimal targeting requirements have allowed most conserved miRNAs to acquire large target cohorts, thus imposing strong selection on miRNAs to maintain their seed sequences. Second, the modest pairing needed for repression suggests that evolutionarily nascent miRNAs may generally induce net detrimental, rather than beneficial, regulatory effects. Hence, levels and activities of newly emerged miRNAs are expected to be limited to preserve the status quo of gene expression. In this study, we unexpectedly show that Drosophila testes specifically express a substantial miRNA population that contravenes these tenets. We find that multiple genomic clusters of testis-restricted miRNAs harbor recently evolved miRNAs, whose experimentally verified orthologs exhibit divergent sequences, even within seed regions. Moreover, this class of miRNAs exhibits higher expression and greater phenotypic capacities in transgenic misexpression assays than do non-testis-restricted miRNAs of similar evolutionary age. These observations suggest that these testis-restricted miRNAs may be evolving adaptively, and several methods of evolutionary analysis provide strong support for this notion. Consistent with this, proof-of-principle tests show that orthologous miRNAs with divergent seeds can distinguish target sensors in a species-cognate manner. Finally, we observe that testis-restricted miRNA clusters exhibit extraordinary dynamics of miRNA gene flux in other Drosophila species. Altogether, our findings reveal a surprising tissue-directed influence of miRNA evolution, involving a distinct mode of miRNA function connected to adaptive gene regulation in the testis.