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Related Concept Videos

MicroRNAs01:22

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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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PymiRa: A rapid and accurate classification tool for small non-coding RNAs, including microRNAs.

Zachary G L Scurlock1, Cinzia G Scarpini1, Nicholas Coleman1,2

  • 1Department of Pathology, University of Cambridge, Tennis Court Road, Cambridge, United Kingdom.

Plos Computational Biology
|March 26, 2026
PubMed
Summary

PymiRa is a new Python aligner for identifying and quantifying microRNAs (miRNAs) from sequencing data. This tool enhances small RNA sequencing analysis by combining alignment approaches for improved accuracy and efficiency.

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Area of Science:

  • Bioinformatics
  • Molecular Biology
  • Genomics

Background:

  • Small non-coding RNAs (sncRNAs) play crucial regulatory roles in biological processes.
  • MicroRNAs (miRNAs), a class of sncRNAs, are vital for gene expression regulation and are implicated in disease.
  • Accurate classification of sncRNAs is essential for small RNA sequencing analysis.

Purpose of the Study:

  • To develop a novel Python-based aligner, PymiRa, for the identification and quantification of miRNAs.
  • To improve upon existing alignment methods for sncRNA analysis by combining different algorithmic approaches.
  • To provide a fast, accurate, and accessible tool for sncRNA identification in research pipelines.

Main Methods:

  • Development of PymiRa, a Python aligner utilizing the Burrows-Wheeler algorithm.
  • Alignment of input FASTA/FASTQ files against a reference hairpin precursor FASTA file from miRBase.
  • Incorporation of a two-mismatch allowance at the 3' end of reads and accounting for 3' post-transcriptional modifications.

Main Results:

  • PymiRa demonstrates improved results and efficiency compared to previous alignment methods.
  • The aligner accurately identifies and quantifies miRNAs, including those with modifications.
  • PymiRa provides precise counts for sncRNA identification in small RNA sequencing pipelines.

Conclusions:

  • PymiRa is a fast, accurate, and publicly accessible aligner for sncRNA and miRNA identification.
  • The tool facilitates a deeper understanding of sncRNA expression in normal physiology and disease states.
  • PymiRa will be maintained with updates, ensuring its continued relevance in the field.