Dataset Information

MMpred: functional miRNA--mRNA interaction analyses by miRNA expression prediction.

ABSTRACT: BACKGROUND: MicroRNA (miRNA) directed gene repression is an important mechanism of posttranscriptional regulation. Comprehensive analyses of how microRNA influence biological processes requires paired miRNA-mRNA expression datasets. However, a review of both GEO and ArrayExpress repositories revealed few such datasets, which was in stark contrast to the large number of messenger RNA (mRNA) only datasets. It is of interest that numerous primary miRNAs (precursors of microRNA) are known to be co-expressed with coding genes (host genes). RESULTS: We developed a miRNA-mRNA interaction analyses pipeline. The proposed solution is based on two miRNA expression prediction methods - a scaling function and a linear model. Additionally, miRNA-mRNA anti-correlation analyses are used to determine the most probable miRNA gene targets (i.e. the differentially expressed genes under the influence of up- or down-regulated microRNA). Both the consistency and accuracy of the prediction method is ensured by the application of stringent statistical methods. Finally, the predicted targets are subjected to functional enrichment analyses including GO, KEGG and DO, to better understand the predicted interactions. CONCLUSIONS: The MMpred pipeline requires only mRNA expression data as input and is independent of third party miRNA target prediction methods. The method passed extensive numerical validation based on the binding energy between the mature miRNA and 3' UTR region of the target gene. We report that MMpred is capable of generating results similar to that obtained using paired datasets. For the reported test cases we generated consistent output and predicted biological relationships that will help formulate further testable hypotheses.

SUBMITTER: Stempor PA

PROVIDER: S-EPMC3562514 | biostudies-literature | 2012

REPOSITORIES: biostudies-literature

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MMpred: functional miRNA--mRNA interaction analyses by miRNA expression prediction.

Stempor Przemyslaw A PA Cauchi Michael M Wilson Paul P

BMC genomics 20121114

<h4>Background</h4>MicroRNA (miRNA) directed gene repression is an important mechanism of posttranscriptional regulation. Comprehensive analyses of how microRNA influence biological processes requires paired miRNA-mRNA expression datasets. However, a review of both GEO and ArrayExpress repositories revealed few such datasets, which was in stark contrast to the large number of messenger RNA (mRNA) only datasets. It is of interest that numerous primary miRNAs (precursors of microRNA) are known to ...[more]

PMID: 23151045

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Project description:MotivationThe interaction of miRNA and lncRNA is known to be important for gene regulations. However, not many computational approaches have been developed to analyze known interactions and predict the unknown ones. Given that there are now more evidences that suggest that lncRNA-miRNA interactions are closely related to their relative expression levels in the form of a titration mechanism, we analyzed the patterns in large-scale expression profiles of known lncRNA-miRNA interactions. From these uncovered patterns, we noticed that lncRNAs tend to interact collaboratively with miRNAs of similar expression profiles, and vice versa.ResultsBy representing known interaction between lncRNA and miRNA as a bipartite graph, we propose here a technique, called EPLMI, to construct a prediction model from such a graph. EPLMI performs its tasks based on the assumption that lncRNAs that are highly similar to each other tend to have similar interaction or non-interaction patterns with miRNAs and vice versa. The effectiveness of the prediction model so constructed has been evaluated using the latest dataset of lncRNA-miRNA interactions. The results show that the prediction model can achieve AUCs of 0.8522 and 0.8447 ± 0.0017 based on leave-one-out cross validation and 5-fold cross validation. Using this model, we show that lncRNA-miRNA interactions can be reliably predicted. We also show that we can use it to select the most likely lncRNA targets that specific miRNAs would interact with. We believe that the prediction models discovered by EPLMI can yield great insights for further research on ceRNA regulation network. To the best of our knowledge, EPLMI is the first technique that is developed for large-scale lncRNA-miRNA interaction profiling.Availability and implementationMatlab codes and dataset are available at https://github.com/yahuang1991polyu/EPLMI/.Contactyu-an.huang@connect.polyu.hk or zhuhongyou@ms.xjb.ac.cn.Supplementary informationSupplementary data are available at Bioinformatics online.

Project description:miRNAs are small non-coding RNAs that regulate gene expression by binding to the 3'-UTR of genes. Many recent studies have reported that miRNAs play important biological roles by regulating specific mRNAs or genes. Many sequence-based target prediction algorithms have been developed to predict miRNA targets. However, these methods are not designed for condition-specific target predictions and produce many false positives; thus, expression-based target prediction algorithms have been developed for condition-specific target predictions. A typical strategy to utilize expression data is to leverage the negative control roles of miRNAs on genes. To control false positives, a stringent cutoff value is typically set, but in this case, these methods tend to reject many true target relationships, i.e., false negatives. To overcome these limitations, additional information should be utilized. The literature is probably the best resource that we can utilize. Recent literature mining systems compile millions of articles with experiments designed for specific biological questions, and the systems provide a function to search for specific information. To utilize the literature information, we used a literature mining system, BEST, that automatically extracts information from the literature in PubMed and that allows the user to perform searches of the literature with any English words. By integrating omics data analysis methods and BEST, we developed Context-MMIA, a miRNA-mRNA target prediction method that combines expression data analysis results and the literature information extracted based on the user-specified context. In the pathway enrichment analysis using genes included in the top 200 miRNA-targets, Context-MMIA outperformed the four existing target prediction methods that we tested. In another test on whether prediction methods can re-produce experimentally validated target relationships, Context-MMIA outperformed the four existing target prediction methods. In summary, Context-MMIA allows the user to specify a context of the experimental data to predict miRNA targets, and we believe that Context-MMIA is very useful for predicting condition-specific miRNA targets.

Project description:BackgroundLitter size is an important index of mammalian prolificacy and is determined by the ovulation rate. The ovary is a crucial organ for mammalian reproduction and is associated with follicular development, maturation and ovulation. However, prolificacy is influenced by multiple factors, and its molecular regulation in the follicular phase remains unclear.MethodsTen female goats with no significant differences in age and weight were randomly selected and divided into either the high-yielding group (n = 5, HF) or the low-yielding group (n = 5, LF). Ovarian tissues were collected from goats in the follicular phase and used to construct mRNA and miRNA sequencing libraries to analyze transcriptomic variation between high- and low-yield Yunshang black goats. Furthermore, integrated analysis of the differentially expressed (DE) miRNA-mRNA pairs was performed based on their correlation. The STRING database was used to construct a PPI network of the DEGs. RT-qPCR was used to validate the results of the predicted miRNA-mRNA pairs. Luciferase analysis and CCK-8 assay were used to detect the function of the miRNA-mRNA pairs and the proliferation of goat granulosa cells (GCs).ResultsA total of 43,779 known transcripts, 23,067 novel transcripts, 424 known miRNAs and 656 novel miRNAs were identified by RNA-seq in the ovaries from both groups. Through correlation analysis of the miRNA and mRNA expression profiles, 263 negatively correlated miRNA-mRNA pairs were identified in the LF vs. HF comparison. Annotation analysis of the DE miRNA-mRNA pairs identified targets related to biological processes such as "estrogen receptor binding (GO:0030331)", "oogenesis (GO:0048477)", "ovulation cycle process (GO:0022602)" and "ovarian follicle development (GO:0001541)". Subsequently, five KEGG pathways (oocyte meiosis, progesterone-mediated oocyte maturation, GnRH signaling pathway, Notch signaling pathway and TGF-β signaling pathway) were identified in the interaction network related to follicular development, and a PPI network was also constructed. In the network, we found that CDK12, FAM91A1, PGS1, SERTM1, SPAG5, SYNE1, TMEM14A, WNT4, and CAMK2G were the key nodes, all of which were targets of the DE miRNAs. The PPI analysis showed that there was a clear interaction among the CAMK2G, SERTM1, TMEM14A, CDK12, SYNE1 and WNT4 genes. In addition, dual luciferase reporter and CCK-8 assays confirmed that miR-1271-3p suppressed the proliferation of GCs by inhibiting the expression of TXLNA.ConclusionsThese results increase the understanding of the molecular mechanisms underlying goat prolificacy. These results also provide a basis for studying interactions between genes and miRNAs, as well as the functions of the pathways in ovarian tissues involved in goat prolificacy in the follicular phase.

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MMpred: functional miRNA--mRNA interaction analyses by miRNA expression prediction.

Publications

MMpred: functional miRNA--mRNA interaction analyses by miRNA expression prediction.

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