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siRNA - Small Interfering RNAs02:30

siRNA - Small Interfering RNAs

Small interfering RNAs, or siRNAs, are short regulatory RNA molecules that can silence genes post-transcriptionally, as well as the transcriptional level in some cases. siRNAs are important for protecting cells against viral infections and silencing transposable genetic elements.
In the cytoplasm, siRNA is processed from a double-stranded RNA, which comes from either endogenous DNA transcription or exogenous sources like a virus. This double-stranded RNA is then cleaved by the ATP-dependent...
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RNA interference (RNAi) is a process in which a small non-coding RNA molecule blocks the post-transcriptional expression of a gene by binding to its messenger RNA (mRNA) and preventing the protein from being translated.
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Ribosome Profiling

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RNA-seq03:21

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Related Experiment Video

Updated: May 21, 2026

Computational Analysis Tutorial for Chimeric Small Noncoding RNA: Target RNA Sequencing Libraries
07:35

Computational Analysis Tutorial for Chimeric Small Noncoding RNA: Target RNA Sequencing Libraries

Published on: December 1, 2023

Computational identification of sRNA targets.

Brian Tjaden1

  • 1Computer Science Department, Wellesley College, Wellesley, MA, USA. btjaden@wellesley.edu

Methods in Molecular Biology (Clifton, N.J.)
|June 28, 2012
PubMed
Summary

This study introduces TargetRNA, a computational tool for predicting bacterial small noncoding RNA (sRNA) targets. TargetRNA enhances accuracy by integrating base pairing potential with additional RNA interaction features for regulatory target identification.

Area of Science:

  • Bacteriology
  • Molecular Biology
  • Bioinformatics

Background:

  • Small noncoding RNAs (sRNAs) are crucial posttranscriptional regulators in bacteria.
  • sRNAs function by base-pairing with target messenger RNAs.
  • Accurate identification of sRNA targets is essential for understanding bacterial gene regulation.

Purpose of the Study:

  • To present TargetRNA, a computational program designed for predicting sRNA targets in bacteria.
  • To provide a comprehensive guide for utilizing TargetRNA in identifying sRNA regulatory targets.
  • To improve the accuracy of sRNA target prediction beyond simple base pairing potential.

Main Methods:

  • TargetRNA identifies potential sRNA targets by analyzing significant base pairing potential.
  • The program integrates multiple features of RNA interactions, not solely relying on base pairing.

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  • A detailed user guide is provided for the application of TargetRNA.

    • Main Results:

    • TargetRNA effectively predicts bacterial sRNA targets.
    • Integration of additional interaction features enhances prediction accuracy.
    • The tool facilitates the discovery of novel sRNA-mRNA regulatory relationships.

    Conclusions:

    • TargetRNA is a valuable tool for researchers studying bacterial gene regulation.
    • The program offers an improved method for identifying sRNA targets.
    • Understanding sRNA-mediated regulation is critical for bacterial physiology and pathogenesis.