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Large intergenic non-coding RNAs (lincRNAs) are a new class of functional transcripts, and aberrant expression of lincRNAs was associated with several human diseases. The genetic variants in lincRNA transcription factor binding sites (TFBSs) can change lincRNA expression, thereby affecting the susceptibility to human diseases. To identify and annotate these functional candidates, we have developed a database SNP@lincTFBS, which is devoted to the exploration and annotation of single nucleotide polymorphisms (SNPs) in potential TFBSs of human lincRNAs. We identified 6,665 SNPs in 6,614 conserved TFBSs of 2,423 human lincRNAs. In addition, with ChIPSeq dataset, we identified 139,576 SNPs in 304,517 transcription factor peaks of 4,813 lincRNAs. We also performed comprehensive annotation for these SNPs using 1000 Genomes Project datasets across 11 populations. Moreover, one of the distinctive features of SNP@lincTFBS is the collection of disease-associated SNPs in the lincRNA TFBSs and SNPs in the TFBSs of disease-associated lincRNAs. The web interface enables both flexible data searches and downloads. Quick search can be query of lincRNA name, SNP identifier, or transcription factor name. SNP@lincTFBS provides significant advances in identification of disease-associated lincRNA variants and improved convenience to interpret the discrepant expression of lincRNAs. The SNP@lincTFBS database is available at http://bioinfo.hrbmu.edu.cn/SNP_lincTFBS.

The application of microRNAs (miRNAs) in the therapeutics of glioma and other human diseases is an area of intense interest. However, it's still a great challenge to interpret the functional consequences of using miRNAs in glioma therapy. Here, we examined paired deep sequencing expression profiles of miRNAs and mRNAs from human glioma cell lines after manipulating the levels of miRNAs miR-181d, -21, and -23b, as well as transcriptional regulators β-catenin, CBP, and STAT3. An integrated approach was used to identify functional miRNA-pathway regulatory networks (MPRNs) responding to each manipulation. MiRNAs were identified to regulate glioma related biological pathways collaboratively after manipulating the level of either post-transcriptional or transcriptional regulators, and functional synergy and crosstalk was observed between different MPRNs. MPRNs responsive to multiple interventions were found to occupy central positions in the comprehensive MPRN (cMPRN) generated by integrating all the six MPRNs. Finally, we identified a core module comprising 14 miRNAs and five pathways that could predict the survival of glioma patients and represent potential targets for glioma therapy. Our results provided novel insight into miRNA regulatory mechanisms implicated in therapeutic interventions and could offer more inspiration to miRNA-based glioma therapy.

MicroRNAs (miRNAs) are a class of small (19-25 nt) non-coding RNAs. This important class of gene regulator downregulates gene expression through sequence-specific binding to the 3'untranslated regions (3'UTRs) of target mRNAs. Several computational target prediction approaches have been developed for predicting miRNA targets. However, the predicted target lists often have high false positive rates. To construct a workable target list for subsequent experimental studies, we need novel approaches to properly rank the candidate targets from traditional methods. We performed a systematic analysis of experimentally validated miRNA targets using functional genomics data, and found significant functional associations between genes that were targeted by the same miRNA. Based on this finding, we developed a miRNA target prioritization method named mirTarPri to rank the predicted target lists from commonly used target prediction methods. Leave-one-out cross validation has proved to be successful in identifying known targets, achieving an AUC score up to 0. 84. Validation in high-throughput data proved that mirTarPri was an unbiased method. Applying mirTarPri to prioritize results of six commonly used target prediction methods allowed us to find more positive targets at the top of the prioritized candidate list. In comparison with other methods, mirTarPri had an outstanding performance in gold standard and CLIP data. mirTarPri was a valuable method to improve the efficacy of current miRNA target prediction methods. We have also developed a web-based server for implementing mirTarPri method, which is freely accessible at http://bioinfo.hrbmu.edu.cn/mirTarPri.