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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...

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Updated: Apr 20, 2026

Pooled shRNA Library Screening to Identify Factors that Modulate a Drug Resistance Phenotype

Pooled shRNA Library Screening to Identify Factors that Modulate a Drug Resistance Phenotype

Published on: June 17, 2022

A computational algorithm to predict shRNA potency.

Simon R V Knott¹, Ashley Maceli¹, Nicolas Erard¹

¹Watson School of Biological Sciences, Howard Hughes Medical Institute, Cold Spring Harbor Laboratory, 1 Bungtown Road, Cold Spring Harbor, New York 11724, USA.

|December 2, 2014

Summary

This summary is machine-generated.

Developing effective short hairpin RNAs (shRNAs) is crucial for RNA interference (RNAi) studies. A new algorithm, shERWOOD, predicts shRNA potency, enabling the design of effective tools for gene knockdown research.

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Area of Science:

Molecular Biology
Genetics
Bioinformatics

Background:

RNA interference (RNAi) efficacy relies on high-quality knockdown tools.
Existing algorithms effectively design siRNAs but not shRNAs.
shRNAs present unique challenges due to lower abundance and processing steps.

Purpose of the Study:

To develop a method for identifying potent short hairpin RNAs (shRNAs).
To create a predictive algorithm for shRNA efficacy.
To enable the *ab initio* design of effective shRNAs for gene knockdown.

Main Methods:

Developed a multiplexed assay to generate a large dataset of shRNA efficacy data (~250,000 data points).
Utilized this data to train and develop the shERWOOD algorithm for predicting shRNA knockdown potential.
Integrated shERWOOD with shRNA design strategies for *ab initio* identification of potent shRNAs.

Main Results:

The shERWOOD algorithm accurately predicts the likelihood of potent target knockdown by any given shRNA.
The developed design strategies allow for the *ab initio* identification of potent shRNAs targeting most gene transcripts.
Validated shRNA designs using orthogonal methods and created genome-wide shRNA collections for humans and mice.

Conclusions:

shERWOOD provides a robust computational approach for designing effective shRNAs.
This method significantly advances the ability to generate reliable RNAi-based experimental tools.
The developed genome-wide shRNA collections will facilitate large-scale functional genomics studies.