MicroRNAs (miRNAs) are short non-coding RNAs that regulate gene expression via binding to the 3' ends of mRNAs. MiRNAs have been associated with many cellular events ascertaining their central role in gene regulation. In order to better understand miRNAs of interest it is of utmost importance to learn about the genomic conservation of these genes.
The massive use of Next-Generation Sequencing (NGS) technologies is uncovering an unexpected amount of variability. The functional characterization of such variability, particularly in the most common form of variation found, the Single Nucleotide Variants (SNVs), has become a priority that needs to be addressed in a systematic way.
RNA interference (RNAi) serves as a powerful and widely used gene silencing tool for basic biological research and is being developed as a therapeutic avenue to suppress disease-causing genes. However, the specificity and safety of RNAi strategies remains under scrutiny because small inhibitory RNAs (siRNAs) induce off-target silencing. Currently, the tools available for designing siRNAs are biased toward efficacy as opposed to specificity.
Experimentally validated co-expression correlations between miRNAs and genes are a valuable resource to corroborate observations about miRNA/mRNA changes after experimental perturbations, as well as compare miRNA target predictions with empirical observations. For example, when a given miRNA is transcribed, true targets of that miRNA should tend to have lower expression levels relative to when the miRNA is not expressed.
RNA-dependent gene silencing is becoming a routine tool used in laboratories worldwide. One of the important remaining hurdles in the selection of the target sequence, if not the most important one, is the designing of tools that have minimal off-target effects (i.e. cleaves only the desired sequence). Increasingly, in the current dawn of the post-genomic era, there is a heavy reliance on tools that are suitable for high-throughput functional genomics, consequently more and more bioinformatic software is becoming available.
Non-coding microRNAs (miRNAs) act as regulators of global protein output. While their major effect is on protein levels of target genes, it has been proven that they also specifically impact on the messenger RNA level of targets. Prominent interest in miRNAs strongly motivates the need for increasing the options available to detect their cellular activity.
The prediction of pairing between microRNAs (miRNAs) and the miRNA recognition elements (MREs) on mRNAs is expected to be an important tool for understanding gene regulation. Here, we show that mRNAs that contain Pumilio recognition elements (PRE) in the proximity of predicted miRNA-binding sites are more likely to form stable secondary structures within their 3'-UTR, and we demonstrated using a PUM1 and PUM2 double knockdown that Pumilio proteins are general regulators of miRNA accessibility.
Research attention has been powered to understand the functional roles of non-coding RNAs (ncRNAs). Many studies have demonstrated their deregulation in cancer and other human disorders. ncRNAs are also present in extracellular human body fluids such as serum and plasma, giving them a great potential as non-invasive biomarkers. However, non-coding RNAs have been relatively recently discovered and a comprehensive database including all of them is still missing.
High-throughput perturbation screens measure the phenotypes of thousands of biological samples under various conditions. The phenotypes measured in the screens are subject to substantial biological and technical variation. At the same time, in order to enable high throughput, it is often impossible to include a large number of replicates, and to randomize their order throughout the screens. Distinguishing true changes in the phenotype from stochastic variation in such experimental designs is extremely challenging, and requires adequate statistical methodology.
MicroRNA (miRNA), which is short non-coding RNA, plays a pivotal role in the regulation of many biological processes and affects the stability and/or translation of mRNA. Recently, machine learning algorithms were developed to predict potential miRNA targets. Most of these methods are robust but are not sensitive to redundant or irrelevant features. Despite their good performance, the relative importance of each feature is still unclear.
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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