During the last few years, several new small regulatory RNAs (sRNAs) have been discovered in bacteria. Most of them act as post-transcriptional regulators by base pairing to a target mRNA, causing translational repression or activation, or mRNA degradation. Numerous sRNAs have already been identified, but the number of experimentally verified targets is considerably lower. Consequently, computational target prediction is in great demand.
High-throughput sequencing technologies enable direct approaches to catalog and analyze snapshots of the total small RNA content of living cells. Characterization of high-throughput sequencing data requires bioinformatic tools offering a wide perspective of the small RNA transcriptome. Here we present SeqBuster, a highly versatile and reliable web-based toolkit to process and analyze large-scale small RNA datasets.
High-throughput gene expression experiments are widely used to identify the role of genes involved in biological conditions of interest. MicroRNAs (miRNA) are regulatory molecules that have been functionally associated with several developmental programs and their deregulation with diverse diseases including cancer.
MicroRNAs (miRNAs) are short abundant non-coding RNAs critical for many cellular processes. Deep sequencing (next-generation sequencing) technologies are being readily used to receive a more accurate depiction of miRNA expression profiles in living cells. This type of analysis is a key step towards improving our understanding of the complexity and mode of miRNA regulation.
MicroRNAs (miRNAs) are studied as key regulators of gene expression involved in different diseases. Several single nucleotide polymorphisms (SNPs) in miRNA genes or target sites (miRNA-related SNPs) have been proved to be associated with human diseases by affecting the miRNA-mediated regulatory function. To systematically analyze miRNA-related SNPs and their effects, we performed a genome-wide scan for SNPs in human pre-miRNAs, miRNA flanking regions, target sites, and designed a pipeline to predict the effects of them on miRNA-target interaction.
Deciphering heterogeneous cellular networks with embedded modules is a great challenge of current systems biology. Experimental and computational studies construct complex networks of molecules that describe various aspects of the cell such as transcriptional regulation, protein interactions and metabolism. Groups of interacting genes and proteins reflect network modules that potentially share regulatory mechanisms and relate to common function. Here, we present GraphWeb, a public web server for biological network analysis and module discovery.
Long non-coding RNAs (lncRNAs) and microRNAs (miRNAs) represent two classes of important non-coding RNAs in eukaryotes. Although these non-coding RNAs have been implicated in organismal development and in various human diseases, surprisingly little is known about their transcriptional regulation. Recent advances in chromatin immunoprecipitation with next-generation DNA sequencing (ChIP-Seq) have provided methods of detecting transcription factor binding sites (TFBSs) with unprecedented sensitivity.
RNA interference (RNAi) has become a powerful genetic approach to systematically dissect gene function on a genome-wide scale. Owing to the penetrance and efficiency of RNAi in invertebrates, model organisms such as Drosophila melanogaster and Caenorhabditis elegans have contributed significantly to the identification of novel components of diverse biological pathways, ranging from early development to fat storage and aging.
Crosslinking and immunoprecipitation (CLIP) protocols have made it possible to identify transcriptome-wide RNA-protein interaction sites. In particular, PAR-CLIP utilizes a photoactivatable nucleoside for more efficient crosslinking. We present an approach, centered on the novel PARalyzer tool, for mapping high-confidence sites from PAR-CLIP deep-sequencing data. We show that PARalyzer delineates sites with a high signal-to-noise ratio.
Four hundred and eighty-one ultraconserved sequences (UCRs) longer than 200 bases were discovered in the genomes of human, mouse and rat. These are DNA sequences showing 100% identity among the three species. UCRs are frequently located at genomic regions involved in cancer, differentially expressed in human leukemias and carcinomas and in some instances regulated by microRNAs (miRNAs). Here we present UCbase & miRfunc, the first database which provides ultraconserved sequences data and shows miRNA function.
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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