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Related Concept Videos

siRNA - Small Interfering RNAs02:30

siRNA - Small Interfering RNAs

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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.
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RNA interference (RNAi) is a cellular mechanism that inhibits gene expression by suppressing its transcription or activating the RNA degradation process. The mechanism was discovered by Andrew Fire and Craig Mello in 1998 in plants. Today, it is observed in almost all eukaryotes, including protozoa, flies, nematodes, insects, parasites, and mammals. This precise cellular mechanism of gene silencing has been developed into a technique that provides an efficient way to identify and determine the...
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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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Soft computing model for optimized siRNA design by identifying off target possibilities using artificial neural

Reena Murali1, Philips George John1, David Peter S2

  • 1Department of Computer Science and Engineering, Rajiv Gandhi Institute of Technology, Kerala, India.

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Summary

This study introduces a novel neural network model to design effective small interfering RNA (siRNA) sequences. The model predicts both gene knockdown efficacy and off-target effects, improving therapeutic applications in bioinformatics.

Keywords:
Area Under CurveArtificial neural networkMessenger RNAOff targetSmall interfering RNA

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Area of Science:

  • Bioinformatics
  • Genomics
  • Molecular Biology

Background:

  • Small interfering RNA (siRNA) is crucial for posttranscriptional gene regulation in research and therapeutics.
  • Designing effective siRNA requires balancing target gene knockdown with minimizing off-target effects.
  • Current methods often struggle to comprehensively assess both siRNA efficacy and off-target potential.

Purpose of the Study:

  • To develop a novel neural network model for identifying effective siRNA sequences.
  • To simultaneously evaluate siRNA inhibition efficacy and off-target effects.
  • To provide a tool for designing safer and more potent siRNA therapeutics.

Main Methods:

  • A neural network model incorporating whole stacking energy (ΔG) was designed.
  • The model analyzes siRNA inhibition efficacy against target genes.
  • Off-target possibilities were assessed by comparing siRNA sequences against a gene database.

Main Results:

  • The model achieved high performance with a Pearson Correlation Coefficient (R) of 0.74 and an Area Under Curve (AUC) of 0.906.
  • An optimal whole stacking energy threshold of ≥-34.6 kcal/mol was identified.
  • The tool successfully lists siRNAs with their efficacy and potential off-target matches.

Conclusions:

  • The proposed neural network model effectively predicts combined siRNA efficacy and off-target effects.
  • This approach offers a significant advancement for designing therapeutic siRNA.
  • The developed software is available as a desktop application for bioinformatics applications.