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Related Concept Videos

MicroRNAs01:22

MicroRNAs

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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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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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TimiRGeN: R/Bioconductor package for time series microRNA-mRNA integration and analysis.

K Patel1, S Chandrasegaran1, I M Clark2

  • 1Campus for Ageing and Vitality, Biosciences Institute, Newcastle University, Newcastle upon Tyne NE4 5PL, UK.

Bioinformatics (Oxford, England)
|May 16, 2021
PubMed
Summary
This summary is machine-generated.

TimiRGeN is a new R package for analyzing longitudinal miRNA-mRNA data to build gene regulatory networks (GRNs). It identifies key interactions and aids in functional analysis for biological discovery.

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Systems Biology

Background:

  • Longitudinal datasets and gene regulatory networks (GRNs) are crucial for understanding microRNA (miRNA)-mRNA interactions.
  • Existing computational tools lack the ability to integrate, functionally analyze, and generate detailed networks from longitudinal miRNA-mRNA data.

Purpose of the Study:

  • To introduce TimiRGeN, a novel R package designed for the analysis of longitudinal miRNA-mRNA datasets.
  • To enable the identification and visualization of miRNA-mRNA interactions within signaling pathways.

Main Methods:

  • TimiRGeN utilizes time-point-based differential expression results to identify significant miRNA-mRNA interactions.
  • The package allows for visualization of these interactions within R or export to external platforms like PathVisio and Cytoscape.
  • It facilitates hypothesis generation for further experimental or computational investigation.

Main Results:

  • TimiRGeN successfully identifies miRNA-mRNA interactions from longitudinal data.
  • The package generates visualizable networks that highlight key interactions influencing biological pathways.
  • Outputs are suitable for guiding in vitro experiments and in silico GRN construction.

Conclusions:

  • TimiRGeN addresses the need for a specialized tool to analyze longitudinal miRNA-mRNA data.
  • The package enhances the study of gene regulation by providing tools for network construction and functional analysis.
  • It supports researchers in generating testable hypotheses and advancing the understanding of complex biological systems.