RIP-chip is a high-throughput method to identify mRNAs that are targeted by RNA-binding proteins. The protein of interest is immunoprecipitated, and the identity and relative amount of mRNA associated with it is measured on microarrays. Even if a variety of methods is available to analyse microarray data, e.g. to detect differentially regulated genes, the additional experimental steps in RIP-chip require specialized methods.
Cross-linking immunoprecipitation coupled with high-throughput sequencing (CLIP-Seq) has made it possible to identify the targeting sites of RNA-binding proteins in various cell culture systems and tissue types on a genome-wide scale. Here we present a novel model-based approach (MiClip) to identify high-confidence protein-RNA binding sites from CLIP-seq datasets. This approach assigns a probability score for each potential binding site to help prioritize subsequent validation experiments. The MiClip algorithm has been tested in both HITS-CLIP and PAR-CLIP datasets.
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.
The surprising observation that virtually the entire human genome is transcribed means we know little about the function of many emerging classes of RNAs, except their astounding diversities. Traditional RNA function prediction methods rely on sequence or alignment information, which are limited in their abilities to classify the various collections of non-coding RNAs (ncRNAs). To address this, we developed Classification of RNAs by Analysis of Length (CoRAL), a machine learning-based approach for classification of RNA molecules.
Systematic identification of microRNA (miRNA) targets remains a challenge. The miRNA overexpression coupled with genome-wide expression profiling is a promising new approach and calls for a new method that integrates expression and sequence information.
Expression levels of mRNAs are among other factors regulated by microRNAs. A particular microRNA can bind specifically to several target mRNAs and lead to their degradation. Expression levels of both, mRNAs and microRNAs, can be obtained by microarray experiments. In order to increase the power of detecting microRNAs that are differentially expressed between two different groups of samples, we incorporate expression levels of their related target gene sets.
Small RNA sequencing and degradome sequencing (also known as parallel analysis of RNA ends) have provided rich information on the microRNA (miRNA) and its cleaved mRNA targets on a genome-wide scale in plants, but no computational tools have been developed to effectively and conveniently deconvolute the miRNA-target interaction (MTI).
MicroRNAs (miRNAs) measured from blood samples are promising minimally invasive biomarker candidates that have been extensively studied in several case-control studies. However, the influence of age and sex as confounding variables remains largely unknown.
A useful step for understanding the function of microRNAs (miRNA) or siRNAs is the detection of their effects on genome-wide expression profiles. Typically, approaches look for enrichment of words in the 3(')UTR sequences of the most deregulated genes. A number of tools are available for this purpose, but they require either in-depth computational knowledge, filtered 3(')UTR sequences for the genome of interest, or a set of genes acquired through an arbitrary expression cutoff.
Non-coding elements such as miRNAs play key regulatory roles in living systems. These ultra-short, ~21 bp long, RNA molecules are derived from their hairpin precursors and usually participate in negative gene regulation by binding the target mRNAs. Discovering miRNA candidate regions across the genome has been a challenging problem. Most of the existing tools work reliably only for limited datasets.
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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