MicroRNAs (miRNAs) are small noncoding RNAs, which play significant roles as posttranscriptional regulators. The functions of animal miRNAs are generally based on complementarity for their 5' components. Although several computational miRNA target-gene prediction methods have been proposed, they still have limitations in revealing actual target genes.
MicroRNAs are short (approximately 22 nucleotides) noncoding RNAs that regulate the stability and translation of mRNA targets. A number of computational algorithms have been developed to help predict which microRNAs are likely to regulate which genes. Gene expression profiling of biological systems where microRNAs might be active can yield hundreds of differentially expressed genes. The commonly used public microRNA target prediction databases facilitate gene-by-gene searches.
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.
The use of exogenous small interfering RNAs (siRNAs) for gene silencing has quickly become a widespread molecular tool providing a powerful means for gene functional study and new drug target identification. Although considerable progress has been made recently in understanding how the RNAi pathway mediates gene silencing, the design of potent siRNAs remains challenging.
Mature microRNAs (miRNAs) are processed from long hairpin transcripts. Even though it is only the first of several steps, the initial Drosha processing defines the mature product and is characteristic for all miRNA genes. Methods that can separate between true and false processing sites are therefore essential to miRNA gene discovery.
In this article, we show that the classification of human precursor microRNA (pre-miRNAs) hairpins from both genome pseudo hairpins and other non-coding RNAs (ncRNAs) is a common and essential requirement for both comparative and non-comparative computational recognition of human miRNA genes. However, the existing computational methods do not address this issue completely or successfully. Here we present the development of an effective classifier system (named as microPred) for this classification problem by using appropriate machine learning techniques.
With the increasing use of post-genomics techniques to examine a wide variety of biological systems in laboratories throughout the world, scientists are often presented with lists of genes that they must make sense of. A consistently challenging problem is that of defining co-regulated genes within those gene lists. In recent years, microRNAs have emerged as a mechanism for regulating several cellular processes.
mirConnX is a user-friendly web interface for inferring, displaying and parsing mRNA and microRNA (miRNA) gene regulatory networks. mirConnX combines sequence information with gene expression data analysis to create a disease-specific, genome-wide regulatory network. A prior, static network has been constructed for all human and mouse genes. It consists of computationally predicted transcription factor (TF)-gene associations and miRNA target predictions. The prior network is supplemented with known interactions from the literature.
MicroRNAs (miRNAs) are small non-coding RNA molecules that are ~22-nt-long sequences capable of suppressing protein synthesis. Previous research has suggested that miRNAs regulate 30% or more of the human protein-coding genes. The aim of this work is to consider various analyzing scenarios in the identification of miRNA-target interactions, as well as to provide an integrated system that will aid in facilitating investigation on the influence of miRNA targets by alternative splicing and the biological function of miRNAs in biological pathways.
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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