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MiRNAs play an essential role in the networks of gene regulation by inhibiting the translation of target mRNAs. Several computational approaches have been proposed for the prediction of miRNA target-genes. Reports reveal a large fraction of under-predicted or falsely predicted target genes. Thus, there is an imperative need to develop a computational method by which the target mRNAs of existing miRNAs can be correctly identified.

The accurate prediction of a comprehensive set of messenger putative antagomirs against microRNAs (miRNAs) remains an open problem. In particular, a set of putative antagomirs against human miRNA is predicted in this current version of database. We have developed Antagomir database, based on putative antagomirs-miRNA heterodimers. In this work, the human miRNA dataset was used as template to design putative antagomirs, using GC content and secondary structures as parameters. The algorithm used predicted the free energy of unbound antagomirs.

MicroRNAs (miRNAs), a class of endogenous small regulatory RNAs, play important roles in many biological and physiological processes. The perturbations of some miRNAs, which are usually called as onco-microRNAs (onco-miRs), are significantly associated with multiple stages of cancer. Although hundreds of miRNAs have been discovered, the perturbed miRNA regulatory networks and their functions are still poorly understood in cancer. Analyzing the expression patterns of miRNA target genes is a very useful strategy to infer the perturbed miRNA networks.

Interplays between transcription factors (TFs) and microRNAs (miRNAs) in gene regulation are implicated in various physiological processes. It is thus important to identify biologically meaningful network motifs involving both types of regulators to understand the key co-regulatory mechanisms underlying the cellular identity and function. However, existing motif finders do not scale well for large networks and are not designed specifically for co-regulatory networks.

MicroRNAs (miRNAs) regulate disease-relevant metabolic pathways. However, most current pathway identification methods fail to consider miRNAs in addition to genes when analyzing pathways. We developed a powerful method called Subpathway-GMir to construct miRNA-regulated metabolic pathways and to identify miRNA-mediated subpathways by considering condition-specific genes, miRNAs, and pathway topologies. We used Subpathway-GMir to analyze two liver hepatocellular carcinomas (LIHC), one stomach adenocarcinoma (STAD), and one type 2 diabetes (T2D) data sets.

Many RNAs have evolutionarily conserved secondary structures instead of primary sequences. Recently, there are an increasing number of methods being developed with focus on the structural alignments for finding conserved secondary structures as well as common structural motifs in pair-wise or multiple sequences. A challenging task is to search similar structures quickly for structured RNA sequences in large genomic databases since existing methods are too slow to be used in large databases.