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MicroRNA (miRNA) are short, regulatory RNA transcribed from introns (non-coding regions of a gene) or intergenic regions (stretches of DNA present between genes). Several processing steps are required to form biologically active, mature miRNA. The initial transcript, called primary miRNA (pri-mRNA), base-pairs with itself, forming a stem-loop structure. Within the nucleus, an endonuclease enzyme, called Drosha, shortens the stem-loop structure into hairpin-shaped pre-miRNA. After the pre-miRNA...
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MicroRNA (miRNA) are short, regulatory RNA transcribed from introns—non-coding regions of a gene—or intergenic regions—stretches of DNA present between genes. Several processing steps are required to form biologically active, mature miRNA. The initial transcript, called primary miRNA (pri-mRNA), base-pairs with itself forming a stem-loop structure. Within the nucleus, an endonuclease enzyme, called Drosha, shortens the stem-loop structure into hairpin-shaped pre-miRNA. After...
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Lung microRNA Profiling Across the Estrous Cycle in Ozone-exposed Mice
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miRTarVis: an interactive visual analysis tool for microRNA-mRNA expression profile data.

Daekyoung Jung1, Bohyoung Kim2, Robert J Freishtat3

  • 1Department of Computer Science and Engineering, Seoul National University, 1, Gwanak-ro, Gwanak-gu, Seoul, South Korea.

BMC Proceedings
|September 12, 2015
PubMed
Summary

miRTarVis is a novel tool that predicts microRNA (miRNA) targets using expression data and visualizes interactions. This interactive platform aids in understanding miRNA-mRNA networks for biological research.

Keywords:
Expression profileMicroRNATarget predictionVisualizationmRNA

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • MicroRNAs (miRNAs) are key regulators of gene expression.
  • Traditional miRNA target prediction relies on sequence complementarity.
  • Existing tools often lack advanced visualization and novel prediction algorithms.

Purpose of the Study:

  • To develop an interactive visual analysis tool for miRNA target prediction.
  • To integrate novel prediction algorithms with miRNA-mRNA expression profile data.
  • To enhance the understanding of miRNA-target interactions through visualization.

Main Methods:

  • Designed and implemented miRTarVis, an interactive visual analysis tool.
  • Employed Bayesian inference, MINE, correlation, and mutual information analyses for target prediction.
  • Utilized Treemap and node-link diagrams for network visualization.

Main Results:

  • miRTarVis provides an intuitive interface for data loading, filtering, prediction, and visualization.
  • The tool successfully predicts miRNA targets from expression profile data.
  • Visualizations include interactive Treemaps and conventional node-link diagrams.

Conclusions:

  • miRTarVis facilitates the prediction and comprehension of miRNA targets from expression data.
  • A case study on asthma patients demonstrated the tool's utility.
  • The tool aids researchers in exploring complex miRNA-mRNA interactions.