微小RNA起源及功能和动植物基因寻找计算方法

微小RNAs（microRNAs,miRNAs）是长度约为22个核苷酸（nt）的内源性非编码小分子RNA。miRNA作为重要的基因调节因子,通过多种机制抑制其靶mRNA的表达。miRNA的表达和／或功能异常与人类多种疾病密切相关。因此,近年miR—NA与人类疾病的相关研究备受关注,寻找miRNA基因显得尤为重要。过去对miRNA基因进行研究的范围较为局限,获得的新miRNA基因很少。目前,对miRNA基因目录的补充主要依赖于复杂计算工具的发展,随着计算工具的发展获得多种简易的寻找miRNA基因的方法,但对miRNA基因目录的补充仍未能起有效作用。本文在简单介绍动植物miRNA生物起源和功能及作用机制的基础上,主要关注动植物miRNA基因寻找的计算方法,可望为探索动植物miRNAs基因寻找的新的计算方法提供有价值的参考。     

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.     

Identification of miRNA targets with stable isotope labeling by amino acids in cell culture

miRNAs are small noncoding RNAs that regulate gene expression. We have used stable isotope labeling by amino acids in cell culture (SILAC) to investigate the effect of miRNA-1 on the HeLa cell proteome. Expression of 12 out of 504 investigated proteins was repressed by miRNA-1 transfection. This repressed set of genes significantly overlaps with miRNA-1 regulated genes that have been identified with DNA array technology and are predicted by computational methods. Moreover, we find that the 3′-untranslated region for the repressed set are enriched in miRNA-1 complementary sites. Our findings demonstrate that SILAC can be used for miRNA target identification and that one highly expressed miRNA can regulate the levels of many different proteins.     

Identifying miRNA-disease association based on integrating miRNA topological similarity and functional similarity

Background: MicroRNAs (miRNAs) are a significant type of non-coding RNAs, which usually were encoded by endogenous genes with about ~22 nt nucleotides. Accumulating biological experiments have shown that miRNAs have close associations with various human diseases. Although traditional experimental methods achieve great successes in miRNA-disease interaction identification, these methods also have some limitations. Therefore, it is necessary to develop computational method to predict miRNA-disease interactions. Methods: Here, we propose a computational framework (MDVSI) to predict interactions between miRNAs and diseases by integrating miRNA topological similarity and functional similarity. Firstly, the CosRA index is utilized to measure miRNA similarity based on network topological feature. Then, in order to enhance the reliability of miRNA similarity, the functional similarity and CosRA similarity are integrated based on linear weight method. Further, the potential miRNA-disease associations are predicted by using recommendation method. In addition, in order to overcome limitation of recommendation method, for new disease, a new strategy is proposed to predict potential interactions between miRNAs and new disease based on disease functional similarity. Results: To evaluate the performance of different methods, we conduct ten-fold cross validation and de novo test in experiment and compare MDVSI with two the-state-of-art methods. The experimental result shows that MDVSI achieves an AUC of 0.91, which is at least 0.012 higher than other compared methods. Conclusions: In summary, we propose a computational framework (MDSVI) for miRNA-disease interaction prediction. The experiment results demonstrate that it outperforms other the-state-of-the-art methods. Case study shows that it can effectively identify potential miRNA-disease interactions.     

Human microRNA target identification by RRSM

MicroRNAs (miRNAs) are small endogenously expressed non-coding RNAs that regulate target messenger RNAs in various biological processes. In recent years, there have been many studies concentrated on the discovery of new miRNAs and identification of their mRNA targets. Although researchers have identified many miRNAs, few miRNA targets have been identified by actual experimental methods. To expedite the identification of miRNA targets for experimental verification, in the literature approaches based on the sequence or microarray expression analysis have been established to discover the potential miRNA targets. In this study, we focus on the human miRNA target prediction and propose a generalized relative R2 method (RRSM) to find many high-confidence targets. Many targets have been confirmed from previous studies. The targets for several miRNAs discovered by the HITS-CLIP method in a recent study have also been selected by our study.     

A functional assay for microRNA target identification and validation

MicroRNAs (miRNA) are a class of small RNA molecules that regulate numerous critical cellular processes and bind to partially complementary sequences resulting in down-regulation of their target genes. Due to the incomplete homology of the miRNA to its target site identification of miRNA target genes is difficult and currently based on computational algorithms predicting large numbers of potential targets for a given miRNA. To enable the identification of biologically relevant miRNA targets, we describe a novel functional assay based on a 3'-UTR-enriched library and a positive/negative selection strategy. As proof of principle we have used mir-130a and its validated target MAFB to test this strategy. Identification of MAFB and five additional targets and their subsequent confirmation as mir-130a targets by western blot analysis and knockdown experiments validates this strategy for the functional identification of miRNA targets.     

CID-miRNA: a web server for prediction of novel miRNA precursors in human genome

microRNAs (miRNA) are a class of non-protein coding functional RNAs that are thought to regulate expression of target genes by direct interaction with mRNAs. miRNAs have been identified through both experimental and computational methods in a variety of eukaryotic organisms. Though these approaches have been partially successful, there is a need to develop more tools for detection of these RNAs as they are also thought to be present in abundance in many genomes. In this report we describe a tool and a web server, named CID-miRNA, for identification of miRNA precursors in a given DNA sequence, utilising secondary structure-based filtering systems and an algorithm based on stochastic context free grammar trained on human miRNAs. CID-miRNA analyses a given sequence using a web interface, for presence of putative miRNA precursors and the generated output lists all the potential regions that can form miRNA-like structures. It can also scan large genomic sequences for the presence of potential miRNA precursors in its stand-alone form. The web server can be accessed at http://mirna.jnu.ac.in/cidmirna/.     

Characterization of microRNAs from sheep (<Emphasis Type="Italic">Ovis aries</Emphasis>) using computational and experimental analyses

Ovis aries is one of the most important agricultural livestock for meat production, and also is an ideal model organism for biological and comparative genomics studies. Many miRNAs have been reported for their important roles in developmental processes in various animals, but there is limited information about O. aries miRNAs. In this study, combining a computational method based on expressed sequence tag (EST) analysis with experimental identification based on small RNA cDNA library, we identified 31 miRNAs belong to 24 families in sheep, 2 of which were novel miRNAs which had never been previously identified in any species. Especially, we cloned 12 miRNAs from the sheep skeletal muscle, which were good candidate miRNAs to be studied about the miRNA-dependant regulated process of muscle development, and we identified four pairs of miRNA/miRNA* and one pair of miRNA-3p/miRNA-5p from sheep EST sequences. Expression analysis indicated that some miRNAs were expressed in a specific tissue, and the pair of miRNA-3p/miRNA-5p and one pair of miRNA/miRNA* had a similar relative expression pattern in some tissues, respectively. Further, we predicted 120 potential target genes of 31 oar-miRNAs on the 3′UTR of O. aries genes. Gene ontology analysis showed that most of these genes took part in the cellular process and metabolic process. Our results enriched the O. aries miRNA database and provided useful information for investigating biological functions of miRNAs and miRNA* in sheep.     

A GAL4/UAS luciferase system to identify the miRNAs target mRNAs in <Emphasis Type="Italic">Drosophila</Emphasis> S2 cells

MicroRNAs (miRNAs) have been shown to regulate gene expression through the sequence-specific base pairing with their target mRNAs. However, our understanding of the biological roles of miRNAs is still quite limited, and only a handful of miRNAs have been assigned by genetic analysis in part owing to the difficulty in the identification of their targets. Although computational methods have shown to be helpful in the prediction of miRNA targets, a major obstacle has been the lack of quick and efficient experimental procedures to verify these targets. In this report, we describe a UAS/GAL4-based reporter system for this purpose. Our data indicate it an assay of miRNA–target gene interaction, with greater sensitivity over the previously reported methods, and may be useful for more efficient identification/validation the miRNA targets in Drosophila cell lines.     

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。     

The discovery approaches and detection methods of microRNAs

MicroRNAs (miRNAs) are small, highly conserved, non-coding RNAs that regulate gene expression of target mRNAs through cleavage or translational inhibition. Computer-based approaches for miRNA gene identification are being considered as indispensable in miRNAs research. Similarly, experimental approaches for detection of miRNAs are crucial to the testing and validating of computational algorithms. The detection of miRNAs in tissues or cells can supply valuable information for investigating the biological function of these molecules. Selective and highly sensitive detection methods will pave the way for extended understanding of miRNA function within organisms. In this review, we summarize the various computational methods for identification of miRNAs as well as the methodologies that have been developed to detection miRNAs.     

miRTar Hunter: A prediction system for identifying human microRNA target sites

MicroRNAs (miRNAs) are important regulators of gene expression and play crucial roles in many biological processes including apoptosis, differentiation, development, and tumorigenesis. Recent estimates suggest that more than 50% of human protein coding genes may be regulated by miRNAs and that each miRNA may bind to 300–400 target genes. Approximately 1,000 human miRNAs have been identified so far with each having up to hundreds of unique target mRNAs. However, the targets for a majority of these miRNAs have not been identified due to the lack of large-scale experimental detection techniques. Experimental detection of miRNA target sites is a costly and time-consuming process, even though identification of miRNA targets is critical to unraveling their functions in various biological processes. To identify miRNA targets, we developed miRTar Hunter, a novel computational approach for predicting target sites regardless of the presence or absence of a seed match or evolutionary sequence conservation. Our approach is based on a dynamic programming algorithm that incorporates more sequence-specific features and reflects the properties of various types of target sites that determine diverse aspects of complementarities between miRNAs and their targets. We evaluated the performance of our algorithm on 532 known human miRNA:target pairs and 59 experimentally-verified negative miRNA:target pairs, and also compared our method with three popular programs for 481 miRNA:target pairs. miRTar Hunter outperformed three popular existing algorithms in terms of recall and precision, indicating that our unique scheme to quantify the determinants of complementary sites is effective at detecting miRNA targets. miRTar Hunter is now available at http://203.230.194.162/~kbkim.