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Related Concept Videos

MicroRNAs01:22

MicroRNAs

21.6K
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...
21.6K

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Updated: Sep 5, 2025

mirMachine: A One-Stop Shop for Plant miRNA Annotation
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Detecting MicroRNAs in Plant Genomes with miRkwood.

Sylvain Legrand1, Isabelle Guigon2, Hélène Touzet3

  • 1Univ. Lille, CNRS, UMR 8198, Evo-Eco-Paleo, Lille, France.

Methods in Molecular Biology (Clifton, N.J.)
|July 11, 2022
PubMed
Summary

miRkwood is a new software tool for identifying plant microRNAs and their precursors, even without sequencing data. It aids in analyzing genomic annotations, precursor structures, and read distributions for plant research.

Keywords:
BioinformaticsHigh throughput sequencingMicroRNASmall RNA sequencing

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Area of Science:

  • Genomics
  • Bioinformatics
  • Molecular Biology

Background:

  • MicroRNAs (miRNAs) are crucial regulatory molecules in plants.
  • Identifying plant miRNAs and their precursors is essential for understanding gene regulation.
  • Existing tools may have limitations in identifying miRNAs without sequencing data.

Purpose of the Study:

  • To introduce miRkwood, a versatile software tool for plant miRNA and precursor identification.
  • To provide a comprehensive solution for miRNA analysis, with or without small-RNA sequencing data.
  • To facilitate the exploration and analysis of genomic annotations, precursor structures, and sequencing data.

Main Methods:

  • Development of a novel bioinformatics software, miRkwood.
  • Implementation of algorithms for identifying miRNA precursors within plant genomes.
  • Integration of analysis modules for genomic annotations, secondary structures, alignments, and read distribution.

Main Results:

  • miRkwood successfully identifies microRNAs and their precursors in plant genomes.
  • The software is functional with or without small-RNA sequencing data.
  • Detailed analysis of genomic features, precursor structures, and sequencing data alignment is enabled.

Conclusions:

  • miRkwood offers a robust and flexible platform for plant miRNA research.
  • The tool enhances the ability to discover and characterize plant miRNAs.
  • It provides valuable insights into miRNA biogenesis and function in plants.