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.
MicroRNAs (miRNAs) are important regulatory molecules. A critical step in elucidating miRNA function is identifying potential miRNA targets. However, few reliable tools have been developed for identifying miRNA targets in plants.
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.
We used network-level conservation between pairs of fly (Drosophila melanogaster/D. pseudoobscura) and worm (Caenorhabditis elegans/C. briggsae) genomes to detect highly conserved mRNA motifs in 3' untranslated regions. Many of these elements are complementary to the 5' extremity of known microRNAs (miRNAs), and likely correspond to their target sites. We also identify known targets of RNA-binding proteins, and many novel sites not yet known to be functional.
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.
MicroRNAs (miRNAs) are short RNA species derived from hairpin-forming miRNA precursors (pre-miRNA) and acting as key posttranscriptional regulators. Most computational tools labeled as miRNA predictors are in fact pre-miRNA predictors and provide no information about the putative miRNA location within the pre-miRNA. Sequence and structural features that determine the location of the miRNA, and the extent to which these properties vary from species to species, are poorly understood.
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.
MiRNAs play important roles in many diseases including cancers. However computational prediction of miRNA target genes is challenging and the accuracies of existing methods remain poor. We report mirMark, a new machine learning-based method of miRNA target prediction at the site and UTR levels. This method uses experimentally verified miRNA targets from miRecords and mirTarBase as training sets and considers over 700 features.
Recent discoveries show that most types of small non-coding RNAs (sncRNAs) such as miRNAs, snoRNAs and tRNAs get further processed into putatively active smaller RNA species. Their roles, genetic profiles and underlying processing mechanisms are only partially understood. To find their quantities and characteristics, a proper annotation is essential. Here, we present FlaiMapper, a method that extracts and annotates the locations of sncRNA-derived RNAs (sncdRNAs). These sncdRNAs are often detected in sequencing data and observed as fragments of their precursor sncRNA.
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.
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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