Abstract
BackgroundMicroRNAs (miRNA) are short nucleotides that down-regulate its target genes. Various miRNA target prediction algorithms have used sequence complementarity between miRNA and its targets. Recently, other algorithms tried to improve sequence-based miRNA target prediction by exploiting miRNA-mRNA expression profile data. Some web-based tools are also introduced to help researchers predict targets of miRNAs from miRNA-mRNA expression profile data. A demand for a miRNA-mRNA visual analysis tool that features novel miRNA prediction algorithms and more interactive visualization techniques exists.ResultsWe designed and implemented miRTarVis, which is an interactive visual analysis tool that predicts targets of miRNAs from miRNA-mRNA expression profile data and visualizes the resulting miRNA-target interaction network. miRTarVis has intuitive interface design in accordance with the analysis procedure of load, filter, predict, and visualize. It predicts targets of miRNA by adopting Bayesian inference and MINE analyses, as well as conventional correlation and mutual information analyses. It visualizes a resulting miRNA-mRNA network in an interactive Treemap, as well as a conventional node-link diagram. miRTarVis is available at http://hcil.snu.ac.kr/~rati/miRTarVis/index.html.ConclusionsWe reported findings from miRNA-mRNA expression profile data of asthma patients using miRTarVis in a case study. miRTarVis helps to predict and understand targets of miRNA from miRNA-mRNA expression profile data.
Highlights
MicroRNAs are short nucleotides that down-regulate its target genes
We developed miRTarVis, an interactive visual analysis tool that predicts targets of miRNAs from miRNA-mRNA expression profile data and visualizes miRNA-target interaction network that is derived from the prediction
An important step in our analyses was to define the set of lung mRNA responses to these adipocyte-derived exosomes
Summary
MicroRNAs (miRNA) are short nucleotides that down-regulate its target genes. Various miRNA target prediction algorithms have used sequence complementarity between miRNA and its targets. Other algorithms tried to improve sequence-based miRNA target prediction by exploiting miRNA-mRNA expression profile data. Some web-based tools are introduced to help researchers predict targets of miRNAs from miRNA-mRNA expression profile data. A demand for a miRNA-mRNA visual analysis tool that features novel miRNA prediction algorithms and more interactive visualization techniques exists. All organisms use selective gene transcription of mRNAs to carry out biological functions. It is recognized that regulatory RNAs (microRNA (miRNA)), long non-coding RNA (lnRNA) play key roles in regulating the stability and translation of existing pools of mRNAs in any cell. The potential interactions between any miRNA-mRNA pair require experimental validation, typically through reporter constructs
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