MicroRNAs (miRNAs), a class of small regulatory RNAs, play important roles in cancer initiation, progression and therapy. MiRNAs are found to regulate diverse cancer-related processes by targeting a large set of oncogenic and tumor-suppressive genes.
microRNAs (miRNAs) are the most abundant class of small RNAs in mammals. They play an important role in regulation of gene expression by inducing mRNA cleavage or translational inhibition. Each miRNA targets an average of 100-200 genes by binding, preferentially, to their 3' UTRs by means of partial sequence complementarity. Most miRNAs are localized within transcriptional units, termed host genes, and show similar expression behavior with respect to their corresponding host genes.
Some of the recent investigations in systems biology have revealed the existence of a complex regulatory network between genes, microRNAs (miRNAs) and transcription factors (TFs). In this paper, we focus on TF to miRNA regulation and provide a novel interface for extracting the list of putative TFs for human miRNAs. A putative TF of an miRNA is considered here as those binding within the close genomic locality of that miRNA with respect to its starting or ending base pair on the chromosome.
MicroRNAs are small noncoding RNAs that play an important role in the regulation of various biological processes through their interaction with cellular messenger RNAs. They are frequently dysregulated in cancer and have shown great potential as tissue-based markers for cancer classification and prognostication. microRNAs are also present in extracellular human body fluids such as serum, plasma, saliva, and urine. Most of circulating microRNAs are present in human plasma and serum cofractionate with the Argonaute2 (Ago2) protein.
To date, a significant amount of research has been conducted for computational modeling of the microRNA (miRNA)-target gene interactions and inferring different kinds of biologically relevant association from their variable expressions, available from microarray experiments. However, topological organization of the miRNA-transcription factor (TF) induced regulatory network has not yet been analyzed at a genome scale. Evidently, by ignoring the regulatory relationship among the constituent molecules, we expose our model to a great deal of noise.
microRNAs (miRNAs) are short non-coding RNAs that regulate gene expression by inhibiting target mRNA genes. Their tissue- and disease-specific expression patterns have immense therapeutic and diagnostic potential. To understand these patterns, a reliable compilation of miRNA and mRNA expression data is required to compare multiple tissue types. Moreover, with the appropriate statistical tools, such a resource could be interrogated to discover functionally related miRNA-mRNA pairs.
RNA secondary structure is required for the proper regulation of the cellular transcriptome. This is because the functionality, processing, localization and stability of RNAs are all dependent on the folding of these molecules into intricate structures through specific base pairing interactions encoded in their primary nucleotide sequences.
MicroRNAs (miRNAs), one type of small RNAs (sRNAs) in plants, play an essential role in gene regulation. Several miRNA databases were established; however, successively generated new datasets need to be collected, organized and analyzed. To this end, we have constructed a plant miRNA knowledge base (PmiRKB) that provides four major functional modules.
MicroRNAs (miRNAs) are short (19-23 nucleotides) non-coding RNAs that bind to sites in the 3'untranslated regions (3'UTR) of a targeted messenger RNA (mRNA). Binding leads to degradation of the transcript or blocked translation resulting in decreased expression of the targeted gene. Single nucleotide polymorphisms (SNPs) have been found in 3'UTRs that disrupt normal miRNA binding or introduce new binding sites and some of these have been associated with disease pathogenesis.
MicroRNAs (miRNAs) are a set of short (19~24 nt) non-coding RNAs that play significant roles as posttranscriptional regulators in animals and plants. The ab initio prediction methods show excellent performance for discovering new pre-miRNAs. While most of these methods can distinguish real pre-miRNAs from pseudo pre-miRNAs, few can predict the positions of miRNAs. Among the existing methods that can also predict the miRNA positions, most of them are designed for mammalian miRNAs, including human and mouse. Minority of methods can predict the positions of plant miRNAs.
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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