In recent years, there have been increasing numbers of transcripts identified that do not encode proteins, many of which are developmentally regulated and appear to have regulatory functions. Here, we describe the construction of a comprehensive mammalian noncoding RNA database (RNAdb) which contains over 800 unique experimentally studied non-coding RNAs (ncRNAs), including many associated with diseases and/or developmental processes.
MicroRNAs (miRNAs) constitute an important class of regulators that are involved in various cellular and disease processes. However, the functional significance of each miRNA is mostly unknown due to the difficulty in identifying target genes and the lack of genome-wide expression data combining miRNAs, mRNAs and proteins. We introduce a novel database, miRGator, that integrates the target prediction, functional analysis, gene expression data and genome annotation.
MicroRNAs (miRs) have been broadly implicated in animal development and disease. We developed a novel computational strategy for the systematic, whole-genome identification of miRs from high throughput sequencing information. This method, miRTRAP, incorporates the mechanisms of miR biogenesis and includes additional criteria regarding the prevalence and quality of small RNAs arising from the antisense strand and neighboring loci.
Exosomes, membrane microvesicles (40-100 nm) secreted by most cell types, can be isolated in several ways while characterizing them is heavily based on electron microscopy and, most importantly, the identification of exosome marker proteins. Researchers rely on the identification of certain exosomal marker proteins including Alix, CD9 and CD63 to confirm the presence of exosomes in their preparations. An evolutionary-conserved set of protein molecules have been identified in most exosomes studied to date.
It has been reported that increasingly microRNAs are associated with diseases. However, the patterns among the microRNA-disease associations remain largely unclear. In this study, in order to dissect the patterns of microRNA-disease associations, we performed a comprehensive analysis to the human microRNA-disease association data, which is manually collected from publications. We built a human microRNA associated disease network. Interestingly, microRNAs tend to show similar or different dysfunctional evidences for the similar or different disease clusters, respectively.
The Pancreatic Expression database (PED, http://www.pancreasexpression.org) has established itself as the main repository for pancreatic-derived -omics data. For the past 3 years, its data content and access have increased substantially. Here we describe several of its new and improved features, such as data content, which now includes over 60,000 measurements derived from transcriptomics, proteomics, genomics and miRNA profiles from various pancreas-centred reports on a broad range of specimen and experimental types.
Recent advances in sequencing technology have created unprecedented opportunities for biological research. However, the increasing throughput of these technologies has created many challenges for data management and analysis. As the demand for sophisticated analyses increases, the development time of software and algorithms is outpacing the speed of traditional publication. As technologies continue to be developed, methods change rapidly, making publications less relevant for users.
Regulation of post-transcriptional gene expression by microRNAs (miRNA) has so far been validated for only a few mRNA targets. Based on the large number of miRNA genes and the possibility that one miRNA might influence gene expression of several targets simultaneously, the quantity of ribo-regulated genes is expected to be much higher. Here, we describe the web tool MicroInspector that will analyse a user-defined RNA sequence, which is typically an mRNA or a part of an mRNA, for the occurrence of binding sites for known and registered miRNAs.
MicroRNAs (miRNAs) play important roles in gene expression regulation in animals and plants. Since plant miRNAs recognize their target mRNAs by near-perfect base pairing, computational sequence similarity search can be used to identify potential targets. A web-based integrated computing system, miRU, has been developed for plant miRNA target gene prediction in any plant, if a large number of sequences are available.
Target prediction for animal microRNAs (miRNAs) has been hindered by the small number of verified targets available to evaluate the accuracy of predicted miRNA-target interactions. Recently, a dataset of 3,404 miRNA-associated mRNA transcripts was identified by immunoprecipitation of the RNA-induced silencing complex components AIN-1 and AIN-2.
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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