Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

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...

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...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

Co-creation environment with cloud virtual reality and real-time artificial intelligence toward the design of molecular robots.

Journal of integrative bioinformatics·2022

Same author

Utility of constraints reflecting system stability on analyses for biological models.

PLoS computational biology·2022

Same author

Prognostic Importance of Multiple Nutrition Screening Indexes for 1-Year Mortality in Hospitalized Acute Decompensated Heart Failure Patients.

Circulation reports·2021

Same author

Modeling a Microtubule Filaments Mesh Structure from Confocal Microscopy Imaging.

Micromachines·2020

Same author

Adaptation of Patterns of Motile Filaments under Dynamic Boundary Conditions.

ACS nano·2019

Same author

Prevalence of Hepatitis A Immunity and Decision-tree Analysis Among Men Who Have Sex With Men and Are Living With Human Immunodeficiency Virus in Tokyo.

Clinical infectious diseases : an official publication of the Infectious Diseases Society of America·2019

Search research articles

Related Experiment Video

Updated: Jul 17, 2026

Identification of Circular RNAs using RNA Sequencing

Identification of Circular RNAs using RNA Sequencing

Published on: November 14, 2019

Selecting effective siRNA sequences by using radial basis function network and decision tree learning.

Shigeru Takasaki¹, Yoshihiro Kawamura, Akihiko Konagaya

¹RIKEN Genomic Sciences Center, Suehiro-cho 1-7-22-E216, Tsurumi-ku, Yokohama, Kanagawa, 230-0045, Japan. takasaki@gsc.riken.jp

BMC Bioinformatics

|January 27, 2007

Summary

New prediction methods using radial basis function networks and decision trees improve short interfering RNA (siRNA) sequence selection for gene silencing. These tools accurately estimate the probability of effective gene knockdown, aiding research in mammalian systems.

More Related Videos

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

MISSION esiRNA for RNAi Screening in Mammalian Cells

MISSION esiRNA for RNAi Screening in Mammalian Cells

Published on: May 12, 2010

Related Experiment Videos

Last Updated: Jul 17, 2026

Identification of Circular RNAs using RNA Sequencing

Identification of Circular RNAs using RNA Sequencing

Published on: November 14, 2019

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

MISSION esiRNA for RNAi Screening in Mammalian Cells

MISSION esiRNA for RNAi Screening in Mammalian Cells

Published on: May 12, 2010

Area of Science:

Molecular Biology
Bioinformatics
Computational Biology

Background:

Short interfering RNA (siRNA) is crucial for gene function studies in mammalian cells.
Existing siRNA design rules show limited consistency and predictive power for gene silencing efficacy.
Previous methods struggle to estimate the probability of a candidate siRNA sequence's effectiveness.

Purpose of the Study:

To develop novel prediction methods for selecting effective siRNA target sequences.
To enable accurate estimation of the probability that a candidate siRNA sequence will achieve gene silencing.
To improve the efficiency and reliability of siRNA-based gene function research.

Main Methods:

Development of two prediction methods: one using supervised learning of a radial basis function (RBF) network, and another using decision tree learning.
Evaluation of methods using a dataset of 196 siRNA sequences across 15 genes, including both effective and ineffective sequences.
Proposal of a combined prediction method integrating RBF network and decision tree learning.

Main Results:

The proposed RBF network and decision tree methods achieved average prediction probabilities of 65% and 56.6% for effective siRNA sequences, respectively.
The combined method predicted an average probability of 68.5% for effective siRNA sequences.
The methods demonstrated high estimation accuracy, distinguishing effectively between active and inactive siRNA sequences (e.g., 32% vs. 65% for RBF).

Conclusions:

Novel prediction methods for effective siRNA sequence selection have been presented.
The developed methods exhibit high accuracy in estimating the probability of gene silencing.
These tools are expected to be valuable for selecting candidate siRNA sequences for a wide range of genes.