A high-throughput approach for associating MicroRNAs with their activity conditions.

Plant microRNAs (miRNAs) are short RNA sequences that bind to target mRNAs and change their expression levels by redirecting their stabilities and marking them for cleavage. In Arabidopsis thaliana, microRNAs have been shown to regulate development and are believed to impact expression both under various conditions, such as stress and stimuli, as well as in specific tissue types. We present a high throughput approach for associating between microRNAs and conditions in which they act, using novel statistical and algorithmic techniques. Our new tool, miRNAXpress, at first computes a (binary) matrix T denoting the potential targets of microRNAs. Then, using T and an additional predefined matrix X indicating expression of genes under various conditions, it produces a new matrix that predicts associations between microRNAs and the conditions in which they act. Thus, the program comprises two main modules that work in tandem to compute the desired output. The first is an efficient target prediction engine that predicts mRNA targets of query microRNAs by evaluating the optimal duplex that could be formed between the two: given a short query RNA, a long target RNA, and a predefined energy cut-off threshold, the program finds and reports all putative binding sites of the query RNA in the target RNA with hybridization energy bounded by the predefined threshold. The second module realizes an association operation that is computed by a method which relies on an efficient t-test to compute the associations. The calculation of the matrix of microRNAs and their potential targets is the computationally intensive part of the work done by miRNAXpress, and therefore an efficient algorithm for this portion facilitates the entire process. Thus, the target prediction engine is based on an efficient approximate hybridization search algorithm whose efficiency is the result of utilizing the sparsity of the search space without sacrificing the optimality of the results. The time complexity of this algorithm is almost linear in the size of a sparse set of locations where base-pairs are stacked at a height of three or more. Thus miRNAXpress is a novel tool for associating between microRNAs and the conditions in which they act. We employed it to conduct a study, using the plant Arabidopsis thaliana as our model organism. By applying miRNAXpress to 98 microRNAs and 380 conditions, some biologically interesting and statistically strong relations were discovered. For example, mir159C activity is possibly a factor in the misresponse of nph4 mutants to phototropic stimulations.     

Refining microRNA target predictions: Sorting the wheat from the chaff

microRNAs are short RNAs that reduce gene expression by binding to their targets. The accurate prediction of microRNA targets is essential to understanding the function of microRNAs. Computational predictions indicate that all human genes may be regulated by microRNAs, with each microRNA possibly targeting thousands of genes. Here we discuss computational methods for identifying mammalian microRNA targets and refining them for further experimental validation. We describe microRNA target prediction resources and procedures and how they integrate with various types of experimental techniques that aim to validate them or further explore their function. We also provide a list of target prediction databases and explain how these are curated.     

k-gram方法识别microRNA前体

MicroRNAs(miRNAs)是动植物中较短的参与调控基因表达的功能性非编码RNA序列.第一个miRNA是通过实验手段发现的,然而通过实验手段识别miRNA在技术上仍然具有很大的挑战性和不完整性.因此,miRNA基因识别需要寻求计算方法来弥补实验方法的不足.提出了一个全新的miRNA前体的识别方法.在构造识别模型中,把初级序列和序列二级结构相结合,采用k-gram方法把序列信息映射到高维特征空间中,然后通过特征选取方法提取特征,并用这些特征为miRNA前体的识别构造了基于SVM的识别模型.同时,采用隐马尔可夫模型(HMM)的学习方法进行了比较.实验结果表明,该方法是有效的,可以达到较高的敏感性和特异性.     

Novel Anti-Apoptotic MicroRNAs 582-5p and 363 Promote Human Glioblastoma Stem Cell Survival via Direct Inhibition of Caspase 3, Caspase 9, and Bim

Glioblastoma is the most common and lethal primary brain tumor. Tumor initiation and recurrence are likely caused by a sub-population of glioblastoma stem cells, which may derive from mutated neural stem and precursor cells. Since CD133 is a stem cell marker for both normal brain and glioblastoma, and to better understand glioblastoma formation and recurrence, we looked for dys-regulated microRNAs in human CD133+ glioblastoma stem cells as opposed to CD133+ neural stem cells isolated from normal human brain. Using FACS sorting of low-passage cell samples followed by microRNA microarray analysis, we found 43 microRNAs that were dys-regulated in common in three separate CD133+ human glioblastomas compared to CD133+ normal neural stem cells. Among these were several microRNAs not previously associated with cancer. We then verified the microRNAs dys-regulated in glioblastoma using quantitative real time PCR and Taqman analysis of the original samples, as well as human GBM stem cell and established cell lines and many human specimens. We show that two candidate oncogenic microRNAs, miR-363 and miR-582-5p, can positively influence glioblastoma survival, as shown by forced expression of the microRNAs and their inhibitors followed by cell number assay, Caspase 3/7 assay, Annexin V apoptosis/fluorescence activated cell sorting, siRNA rescue of microRNA inhibitor treatment, as well as 3′UTR mutagenesis to show luciferase reporter rescue of the most successful targets. miR-582-5p and miR-363 are shown to directly target Caspase 3, Caspase 9, and Bim.     

Finding novel genes in bacterial communities isolated from the environment

MOTIVATION: Novel sequencing techniques can give access to organisms that are difficult to cultivate using conventional methods. When applied to environmental samples, the data generated has some drawbacks, e.g. short length of assembled contigs, in-frame stop codons and frame shifts. Unfortunately, current gene finders cannot circumvent these difficulties. At the same time, the automated prediction of genes is a prerequisite for the increasing amount of genomic sequences to ensure progress in metagenomics. RESULTS: We introduce a novel gene finding algorithm that incorporates features overcoming the short length of the assembled contigs from environmental data, in-frame stop codons as well as frame shifts contained in bacterial sequences. The results show that by searching for sequence similarities in an environmental sample our algorithm is capable of detecting a high fraction of its gene content, depending on the species composition and the overall size of the sample. The method is valuable for hunting novel unknown genes that may be specific for the habitat where the sample is taken. Finally, we show that our algorithm can even exploit the limited information contained in the short reads generated by 454 technology for the prediction of protein coding genes. AVAILABILITY: The program is freely available upon request.     

k-gram方法识别microRNA前体

MicroRNAs (miRNAs) 是动植物中较短的参与调控基因表达的功能性非编码RNA序列. 第一个miRNA是通过实验手段发现的，然而通过实验手段识别miRNA在技术上仍然具有很大的挑战性和不完整性. 因此，miRNA基因识别需要寻求计算方法来弥补实验方法的不足. 提出了一个全新的miRNA前体的识别方法. 在构造识别模型中，把初级序列和序列二级结构相结合，采用k-gram方法把序列信息映射到高维特征空间中，然后通过特征选取方法提取特征，并用这些特征为miRNA前体的识别构造了基于SVM的识别模型. 同时，采用隐马尔可夫模型(HMM)的学习方法进行了比较. 实验结果表明，该方法是有效的，可以达到较高的敏感性和特异性.     

拟南芥中缺铁反应性microRNAs的鉴定

microRNA是一种非编码蛋白质的小分子RNA,参与了植物生长发育及环境胁迫响应的调控,主要通过对靶基因的负调控去影响生物学过程.基于前人对拟南芥全基因组microRNAs及其靶基因的预测,我们找到了靶向15个缺铁响应基因的22个microRNAs(miR158a、miR164c、miR172a、miR1887、miR2111ab、miR3933、miR395ade、miR414、miR828、miR831、miR837-3P、miR837-5P、miR854abcd、miR857、miR861-5P、miR864-5P).对这些microRNAs的启动子进行分析,发现分别有17、10和4个microRNAs启动子中包含缺铁响应元件IDE1、生长素响应元件和乙烯响应元件.进一步通过Poly(T)adaptor RT-PCR方法对这22个microRNAs在缺铁条件下的表达变化做了检测,结果显示,除miR158a和miR837-5P外的20个microRNAs在缺铁条件下的表达变化都有显著差异,且具有时间依赖性.这20个microRNAs可作为缺铁响应的候选microRNAs.     

A comprehensive study of multiple mapping and feature selection for correction strategy in the analysis of small RNAs from SOLiD sequencing

High-throughput sequencing is a powerful tool for discovering and profiling microRNAs (miRNAs) to gain further insights into their biogenesis and function. Due to shorter size, short RNAs from deep sequencing dataset are prone to map to multiple loci with an equal number of mismatches, especially among multicopy miRNA precursors and homologous miRNA genes. Systematic analysis of SOLiD sequencing dataset showed that 37.94% short RNAs could simultaneously map to more than one miRNA precursor, and more short RNAs were found to have multiple genomic loci. Improper selection from candidate loci might lose some mapping information, influence miRNA expression profile or even mislead to identify novel miRNAs. A comprehensive study indicated several potential features for correction strategy: location and distribution of mismatches, quality values, expression profiles of multiple isomiRs (miRNA variants), miRNA* and moRs (miRNA-offset-RNAs) at candidate locus and in its flank sequence. Further studies should develop an approach to correct the widespread phenomenon of multiple mapping based on these features, and improve accuracy of profiling and discovering miRNAs.     

AGenDA: gene prediction by comparative sequence analysis

Comparative sequence analysis is a powerful approach to identify functional elements in genomic sequences. Herein, we describe AGenDA (Alignment-based GENe Detection Algorithm), a novel method for gene prediction that is based on long-range alignment of syntenic regions in eukaryotic genome sequences. Local sequence homologies identified by the DIALIGN program are searched for conserved splice signals to define potential protein-coding exons; these candidate exons are then used to assemble complete gene structures. The performance of our method was tested on a set of 105 human-mouse sequence pairs. These test runs showed that sensitivity and specificity of AGenDA are comparable with the best gene- prediction program that is currently available. However, since our method is based on a completely different type of input information, it can detect genes that are not detectable by standard methods and vice versa. Thus, our approach seems to be a useful addition to existing gene-prediction programs. Availability: DIALIGN is available through the Bielefeld Bioinformatics Server (BiBiServ) at http://bibiserv.techfak.uni-bielefeld.de/dialign/ The gene-prediction program AGenDA described in this paper will be available through the BiBiServ or MIPS web server at http://mips.gsf.de.     

Prediction of Protein Cleavage Site with Feature Selection by Random Forest

Proteinases play critical roles in both intra and extracellular processes by binding and cleaving their protein substrates. The cleavage can either be non-specific as part of degradation during protein catabolism or highly specific as part of proteolytic cascades and signal transduction events. Identification of these targets is extremely challenging. Current computational approaches for predicting cleavage sites are very limited since they mainly represent the amino acid sequences as patterns or frequency matrices. In this work, we developed a novel predictor based on Random Forest algorithm (RF) using maximum relevance minimum redundancy (mRMR) method followed by incremental feature selection (IFS). The features of physicochemical/biochemical properties, sequence conservation, residual disorder, amino acid occurrence frequency, secondary structure and solvent accessibility were utilized to represent the peptides concerned. Here, we compared existing prediction tools which are available for predicting possible cleavage sites in candidate substrates with ours. It is shown that our method makes much more reliable predictions in terms of the overall prediction accuracy. In addition, this predictor allows the use of a wide range of proteinases.     

Predicting microRNA-disease association based on microRNA structural and functional similarity network

Background: Increasing evidences indicate that microRNAs (miRNAs) are functionally related to the development and progression of various human diseases. Inferring disease-related miRNAs can be helpful in promoting disease biomarker detection for the treatment, diagnosis, and prevention of complex diseases. Methods: To improve the prediction accuracy of miRNA-disease association and capture more potential disease-related miRNAs, we constructed a precise miRNA global similarity network (MSFSN) via calculating the miRNA similarity based on secondary structures, families, and functions. Results: We tested the network on the classical algorithms: WBSMDA and RWRMDA through the method of leave-one-out cross-validation. Eventually, AUCs of 0.8212 and 0.9657 are obtained, respectively. Also, the proposed MSFSN is applied to three cancers for breast neoplasms, hepatocellular carcinoma, and prostate neoplasms. Consequently, 82%, 76%, and 82% of the top 50 potential miRNAs for these diseases are respectively validated by the miRNA-disease associations database miR2Disease and oncomiRDB. Conclusion: Therefore, MSFSN provides a novel miRNA similarity network combining precise function network with global structure network of miRNAs to predict the associations between miRNAs and diseases in various models.     

Prediction of missing enzyme genes in a bacterial metabolic network. Reconstruction of the lysine-degradation pathway of Pseudomonas aeruginosa

The metabolic network is an important biological network which consists of enzymes and chemical compounds. However, a large number of metabolic pathways remains unknown, and most organism-specific metabolic pathways contain many missing enzymes. We present a novel method to identify the genes coding for missing enzymes using available genomic and chemical information from bacterial genomes. The proposed method consists of two steps: (a) estimation of the functional association between the genes with respect to chromosomal proximity and evolutionary association, using supervised network inference; and (b) selection of gene candidates for missing enzymes based on the original candidate score and the chemical reaction information encoded in the EC number. We applied the proposed methods to infer the metabolic network for the bacteria Pseudomonas aeruginosa from two genomic datasets: gene position and phylogenetic profiles. Next, we predicted several missing enzyme genes to reconstruct the lysine-degradation pathway in P. aeruginosa using EC number information. As a result, we identified PA0266 as a putative 5-aminovalerate aminotransferase (EC 2.6.1.48) and PA0265 as a putative glutarate semialdehyde dehydrogenase (EC 1.2.1.20). To verify our prediction, we conducted biochemical assays and examined the activity of the products of the predicted genes, PA0265 and PA0266, in a coupled reaction. We observed that the predicted gene products catalyzed the expected reactions; no activity was seen when both gene products were omitted from the reaction.