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Experimental RNAi

Experimental RNAi

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...

RNA Interference

RNA Interference

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.
This process occurs naturally in cells, often through the activity of genomically-encoded microRNAs. Researchers can take advantage of this mechanism by introducing synthetic RNAs to deactivate specific genes for research or therapeutic purposes. For example, RNAi could be used...

RNA Interference

RNA Interference

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.
This process occurs naturally in cells, often through the activity of genomically-encoded microRNAs. Researchers can take advantage of this mechanism by introducing synthetic RNAs to deactivate specific genes for research or therapeutic purposes. For example, RNAi could be used...

Improving Translational Accuracy

Improving Translational Accuracy

Base complementarity between the three base pairs of mRNA codon and the tRNA anticodon is not a failsafe mechanism. Inaccuracies can range from a single mismatch to no correct base pairing at all. The free energy difference between the correct and nearly correct base pairs can be as small as 3 kcal/ mol. With complementarity being the only proofreading step, the estimated error frequency would be one wrong amino acid in every 100 amino acids incorporated. However, error frequencies observed in...

Improving Translational Accuracy

Improving Translational Accuracy

Base complementarity between the three base pairs of mRNA codon and the tRNA anticodon is not a failsafe mechanism. Inaccuracies can range from a single mismatch to no correct base pairing at all. The free energy difference between the correct and nearly correct base pairs can be as small as 3 kcal/ mol. With complementarity being the only proofreading step, the estimated error frequency would be one wrong amino acid in every 100 amino acids incorporated. However, error frequencies observed in...

siRNA - Small Interfering RNAs

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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Related Experiment Video

Updated: Jul 14, 2026

Generating the Transcriptional Regulation View of Transcriptomic Features for Prediction Task and Dark Biomarker Detection on Small Datasets

Generating the Transcriptional Regulation View of Transcriptomic Features for Prediction Task and Dark Biomarker Detection on Small Datasets

Published on: March 1, 2024

Improving model predictions for RNA interference activities that use support vector machine regression by combining

Andrew S Peek¹

¹Department of Bioinformatics, Integrated DNA Technologies, Inc., Coralville, IA 52241, USA. apeek@idtdna.com

BMC Bioinformatics

|June 8, 2007

Summary

Predicting RNA interference (RNAi) efficacy involves analyzing sequence features. Support Vector Machine (SVM) models identify key factors like nucleotide composition and thermodynamics for effective siRNA design.

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Generating the Transcriptional Regulation View of Transcriptomic Features for Prediction Task and Dark Biomarker Detection on Small Datasets

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Area of Science:

Molecular Biology
Bioinformatics

Background:

RNA interference (RNAi) is a natural process for suppressing gene expression by targeting RNA sequences.
Understanding the factors influencing small interfering RNA (siRNA) efficacy is crucial for its application.

Purpose of the Study:

To quantitatively model RNA interference activities using a Support Vector Machine (SVM) regression approach.
To identify and rank features that predict the efficacy of siRNA sequences.

Main Methods:

Comparison of eight feature mapping methods for building SVM regression models.
Utilized position-specific nucleotide compositions, N-grams, and thermodynamic properties as predictive features.
Assessed the contribution of intramolecular and target strand secondary structures.

Main Results:

Position-specific nucleotide compositions are primary predictors of siRNA efficacy.
Sequence motifs (N-grams) and guide-passenger strand thermodynamics are secondary predictive factors.
The 5' base position of the guide strand is the most informative feature for predicting efficacy.

Conclusions:

The study highlights the relative biological importance of different sequence features in RNAi.
SVM models integrating these features demonstrate increased predictive accuracy for siRNA activity.
Careful feature selection is essential to maintain predictive power while simplifying models.