The use of exogenous small interfering RNAs (siRNAs) for gene silencing has quickly become a widespread molecular tool providing a powerful means for gene functional study and new drug target identification. Although considerable progress has been made recently in understanding how the RNAi pathway mediates gene silencing, the design of potent siRNAs remains challenging.
MicroRNAs have recently emerged as a major class of regulatory molecules involved in a broad range of biological processes and complex diseases. Construction of miRNA-target regulatory networks can provide useful information for the study and diagnosis of complex diseases. Many sequence-based and evolutionary information-based methods have been developed to identify miRNA-mRNA targeting relationships.
One of the main goals in cancer studies including high-throughput microRNA (miRNA) and mRNA data is to find and assess prognostic signatures capable of predicting clinical outcome. Both mRNA and miRNA expression changes in cancer diseases are described to reflect clinical characteristics like staging and prognosis. Furthermore, miRNA abundance can directly affect target transcripts and translation in tumor cells. Prediction models are trained to identify either mRNA or miRNA signatures for patient stratification.
miRNAs are small RNA molecules (' 22nt) that interact with their corresponding target mRNAs inhibiting the translation of the mRNA into proteins and cleaving the target mRNA. This second effect diminishes the overall expression of the target mRNA. Several miRNA-mRNA relationship databases have been deployed, most of them based on sequence complementarities. However, the number of false positives in these databases is large and they do not overlap completely.
Identification of microRNA regulatory modules (MiRMs) will aid deciphering aberrant transcriptional regulatory network in cancer but is computationally challenging. Existing methods are stochastic or require a fixed number of regulatory modules.
microRNAs (miRNAs) are important gene regulators at post-transcriptional level, and inferring miRNA-mRNA regulatory relationships is a crucial problem. Consequently, several computational methods of predicting miRNA targets have been proposed using expression data with or without sequence based miRNA target information. A typical procedure for applying and evaluating such a method is i) collecting matched miRNA and mRNA expression profiles in a specific condition, e.g.
Respiratory epithelial cells are the primary target of influenza virus infection in human. However, the molecular mechanisms of airway epithelial cell responses to viral infection are not fully understood. Revealing genome-wide transcriptional and post-transcriptional regulatory relationships can further advance our understanding of this problem, which motivates the development of novel and more efficient computational methods to simultaneously infer the transcriptional and post-transcriptional regulatory networks.
MicroRNAs (miRNAs) play important roles in general biological processes and diseases pathogenesis. Identifying miRNA target genes is an essential step to fully understand the regulatory effects of miRNAs. Many computational methods based on the sequence complementary rules and the miRNA and mRNA expression profiles have been developed for this purpose. It is noted that there have been many sequence features of miRNA targets available, including the context features of the target sites, the thermodynamic stability and the accessibility energy for miRNA-mRNA interaction.
Our database aim to make it easy for researchers to find the right tools or data source for their own specific study in miRNA field. Now we have collect >1000 tools. This database will update when every new 100 tools add in. Last update time 22nd April, 2018 (v1.2).
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