miRGator v3.0: a microRNA portal for deep sequencing, expression profiling and mRNA targeting

Abstract

Biogenesis and molecular function are two key subjects in the field of microRNA (miRNA) research. Deep sequencing has become the principal technique in cataloging of miRNA repertoire and generating expression profiles in an unbiased manner. Here, we describe the miRGator v3.0 update (http://mirgator.kobic.re.kr) that compiled the deep sequencing miRNA data available in public and implemented several novel tools to facilitate exploration of massive data. The miR-seq browser supports users to examine short read alignment with the secondary structure and read count information available in concurrent windows. Features such as sequence editing, sorting, ordering, import and export of user data would be of great utility for studying iso-miRs, miRNA editing and modifications. miRNA–target relation is essential for understanding miRNA function. Coexpression analysis of miRNA and target mRNAs, based on miRNA-seq and RNA-seq data from the same sample, is visualized in the heat-map and network views where users can investigate the inverse correlation of gene expression and target relations, compiled from various databases of predicted and validated targets. By keeping datasets and analytic tools up-to-date, miRGator should continue to serve as an integrated resource for biogenesis and functional investigation of miRNAs.

Identifying and annotating human bifunctional RNAs reveals their versatile functions

“…Moreover, we also found evidences for supporting the interaction between bifunctional RNAs and miRNAs in such a way that the expression of a number of miRNAs was negatively correlated with that of some bifunctional RNA genes in disparate human cancers or tissues. Besides, expression correlation between bifunctional RNAs and miRNAs extracted from miRGator (Cho et al, 2013) also suggests that bifunctional RNAs might be targeted by different miRNAs under distinct conditions. Interestingly, 26 bifuncational RNAs have the potential to act as ceRNAs to interfere the expression of corresponding genes by competing with the same targeting miRNA.…”

“…10 Figure 5 Count distribution of inverse expression correlation between miRNAs and bifunctional RNAs in different cancer or normal tissues. This figure shows the number of negative expression correlation (r≤-0.3) between bifunctional RNAs and miRNAs in each data set obtained from miRGator V3.0 (Cho et al, 2013), which could provide the evidence that miRNAs may target bifunctional RNAs to regulate their expression.…”

“…To further interrogate whether miRNAs are targeted by those bifunctional RNAs in normal or cancer tissues of human, we explored the expression correlation between miRNAs and their targets in miRGator V3.0 (Cho et al, 2013). Expression correlations of miRNA-RNA cataloged in miRGator were calculated with the expression data of miRNA and RNA from the same samples, which is crucial for minimizing the biological bias.…”

“…In order to investigate the interactions between bifunctional RNAs and miRNA or proteins, we obtained the interactions of miRNA-ncRNA and protein-ncRNA from starBase v2.0 (Li et al, 2014) to construct corresponding networks. We also examined the expression correlatio between bifunctional RNAs and their targeting miRNAs using miRGator V3.0 (Cho et al, 2013) in different cancer and normal tissues. To interrogate whether bifunctional RNAs could act as ceRNAs to regulate the expression of relevant genes, we checked their existence in the ceRNA candidates cataloged in lnCeDB database (Das et al, 2014).…”

Estudos sobre a organização genômica, evolução e expressão de microRNAs

“…Os scores previamente computados para alinhamentos entre espécies de vertebrados foram utilizados para miRNAs conservados além de mamíferos (classes de idade 1 e 2-4). Para miRNAs conservados em mamíferos placentários (classe de idade 5-6), utilizamos os scores compilados para alinhamentos de mamíferos, e para miRNAs conservados em primatas, utilizamos os scores compilados a partir de alinhamentos entre primatas (classe de idade [7][8][9][10][11][12]. O PhyloP score de cada precursor foi determinado pela média dos scores das bases individuais.…”