miRNA is known to regulate up to several hundreds coding genes, thus the integrated analysis of miRNA and mRNA expression data is an important problem. Unfortunately, the integrated analysis is challenging since it needs to consider expression data of two different types, miRNA and mRNA, and target relationship between miRNA and mRNA is not clear, especially when microarray data is used.
During recent years, miRNAs have been shown to play important roles in the regulation of gene expression. Accordingly, much effort has been put into the discovery of novel uncharacterized miRNAs in various organisms. miRNAs are structurally defined by a hairpin-loop structure recognized by the two-step processing apparatus, Drosha and Dicer, necessary for the production of mature ~ 22-nucleotide miRNA guide strands.
Several methods exist for the prediction of precursor miRNAs (pre-miRNAs) in genomic or sRNA-seq (small RNA sequences) data produced by NGS (Next Generation Sequencing). One key information used for this task is the characteristic hairpin structure adopted by pre-miRNAs, that in general are identified using RNA folders whose complexity is cubic in the size of the input. The vast majority of pre-miRNA predictors then rely on further information learned from previously validated miRNAs from the same or a closely related genome for the final prediction of new miRNAs.
The importance of RNA sequence analysis has been increasing since the discovery of various types of non-coding RNAs transcribed in animal cells. Conventional RNA sequence analyses have mainly focused on structured regions, which are stabilized by the stacking energies acting on adjacent base pairs. On the other hand, recent findings regarding the mechanisms of small interfering RNAs (siRNAs) and transcription regulation by microRNAs (miRNAs) indicate the importance of analyzing accessible regions where no base pairs exist.
RNA secondary structure prediction by energy minimization is the central computational tool for the analysis of structural non-coding RNAs and their interactions. Sparsification has been successfully applied to improve the time efficiency of various structure prediction algorithms while guaranteeing the same result; however, for many such folding problems, space efficiency is of even greater concern, particularly for long RNA sequences.
MiRNAs play a critical role in the response of plants to abiotic and biotic stress. However, the functions of most plant miRNAs remain unknown. Inferring these functions from miRNA functional similarity would thus be useful. This study proposes a new method, called PPImiRFS, for inferring miRNA functional similarity.
MicroRNAs (miRNAs) are important regulators of gene expression and play crucial roles in many biological processes including apoptosis, differentiation, development, and tumorigenesis. Recent estimates suggest that more than 50% of human protein coding genes may be regulated by miRNAs and that each miRNA may bind to 300-400 target genes. Approximately 1,000 human miRNAs have been identified so far with each having up to hundreds of unique target mRNAs.
Being involved in many important biological processes, miRNAs can regulate gene expression by targeting mRNAs to facilitate their degradation or translational inhibition. Many miRNA sequencing studies reveal that miRNA variations such as isomiRs and "arm switching" are biologically relevant. However, existing standalone tools usually do not provide comprehensive, detailed information on miRNA variations. To deepen our understanding of miRNA variability, we developed a new standalone tool called "mirPRo" to quantify known miRNAs and predict novel miRNAs.
BACKGROUND: RNA inverse folding is the problem of finding one or more sequences that fold into a user-specified target structure s 0, i.e. whose minimum free energy secondary structure is identical to the target s 0. Here we consider the ensemble of all RNA sequences that have low free energy with respect to a given target s 0. RESULTS: We introduce the program RNAdualPF, which computes the dual partition function Z (∗), defined as the sum of Boltzmann factors exp(-E(a,s 0)/RT) of all RNA nucleotide sequences a compatible with target structure s 0.
In the last few decades, technological and experimental advancements have enabled a more precise understanding of the mode of action of drugs with respect to human cell signaling pathways and have positively influenced the design of new drug compounds. However, as the design of compounds has become increasingly target-specific, the overall effects of a drug on adjacent cellular signaling pathways remain difficult to predict because of the complexity of the interactions involved. Off-target effects of drugs are known to influence their efficacy and safety.
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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