MicroRNAs (miRNAs) are approximately 22-nt RNA segments that are involved in the regulation of protein expression primarily by binding to one or more target sites on an mRNA transcript and inhibiting translation. MicroRNAs are likely to factor into multiple developmental pathways, multiple mechanisms of gene regulation, and underlie an array of inherited disease processes and phenotypic determinants. Several computational programs exist to predict miRNA targets in mammals, fruit flies, worms, and plants.
MicroRNAs (miRNAs) are an important class of small noncoding RNAs capable of regulating other genes' expression. Much progress has been made in computational target prediction of miRNAs in recent years. More than 10 miRNA target prediction programs have been established, yet, the prediction of animal miRNA targets remains a challenging task. We have developed miRecords, an integrated resource for animal miRNA-target interactions.
MicroRNAs (miRNAs) represent an important class of small non-coding RNAs (sRNAs) that regulate gene expression by targeting messenger RNAs. However, assigning miRNAs to their regulatory target genes remains technically challenging. Recently, high-throughput CLIP-Seq and degradome sequencing (Degradome-Seq) methods have been applied to identify the sites of Argonaute interaction and miRNA cleavage sites, respectively.
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.
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.
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.
In animals, RNA binding proteins (RBPs) and microRNAs (miRNAs) post-transcriptionally regulate the expression of virtually all genes by binding to RNA. Recent advances in experimental and computational methods facilitate transcriptome-wide mapping of these interactions. It is thought that the combinatorial action of RBPs and miRNAs on target mRNAs form a post-transcriptional regulatory code. We provide a database that supports the quest for deciphering this regulatory code.
Regulatory RNAs often unfold their action via RNA-RNA interaction. Transcriptional gene silencing by means of siRNAs and miRNA as well as snoRNA directed RNA editing rely on this mechanism. Additionally ncRNA regulation in bacteria is mainly based upon RNA duplex formation. Finding putative target sites for newly discovered ncRNAs is a lengthy task as tools for cofolding RNA molecules like RNAcofold and RNAup are too slow for genome-wide search.
MicroRNAs (miRNAs) regulate gene expression at the posttranscriptional level and are therefore important cellular components. As is true for protein-coding genes, the transcription of miRNAs is regulated by transcription factors (TFs), an important class of gene regulators that act at the transcriptional level. The correct regulation of miRNAs by TFs is critical, and increasing evidence indicates that aberrant regulation of miRNAs by TFs can cause phenotypic variations and diseases.
We introduce a biophysical model of miRNA-target interaction and infer its parameters from Argonaute 2 cross-linking and immunoprecipitation data. We show that a substantial fraction of human miRNA target sites are noncanonical and that predicted target-site affinity correlates well with the extent of target destabilization. Our model provides a rigorous biophysical approach to miRNA target identification beyond ad hoc miRNA seed-based methods.