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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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Related Experiment Video

Updated: Jun 16, 2025

Author Spotlight: AQRNA-seq Role in Mapping Small RNAs and Unraveling Protein Translation Mechanisms

Author Spotlight: AQRNA-seq Role in Mapping Small RNAs and Unraveling Protein Translation Mechanisms

Published on: February 2, 2024

Improved sub-genomic RNA prediction with the ARTIC protocol.

Thomas Baudeau¹, Kristoffer Sahlin²

¹Univ. Lille, CNRS, Centrale Lille, UMR 9189 CRIStAL, F-59000 Lille, France.

Nucleic Acids Research

|August 16, 2024

Summary

This summary is machine-generated.

This study introduces sgENERATE to evaluate SARS-CoV-2 subgenomic RNA (sgRNA) detection tools. A new algorithm, periscope_multi, improves sgRNA detection accuracy from sequencing data.

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Last Updated: Jun 16, 2025

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Identification of Alternative Splicing and Polyadenylation in RNA-seq Data

Identification of Alternative Splicing and Polyadenylation in RNA-seq Data

Published on: June 24, 2021

Area of Science:

Virology
Bioinformatics
Genomics

Background:

Viral subgenomic RNA (sgRNA) is crucial for SARS-CoV-2 replication and evolution.
Existing computational tools are not optimized for viral sgRNA detection from sequencing data.
The ARTIC protocol is widely used for viral sequencing, but analyzing its sgRNA output presents challenges.

Purpose of the Study:

To develop an evaluation pipeline (sgENERATE) for assessing sgRNA detection tools.
To identify limitations in current tools like periscope for sgRNA analysis.
To improve sgRNA detection accuracy by redesigning algorithms for viral-specific data.

Main Methods:

Developed sgENERATE, an evaluation pipeline for sgRNA detection tools.
Evaluated the periscope tool using sgENERATE on ARTIC sequencing data.
Redesigned the periscope algorithm into periscope_multi, utilizing multiple canonical sgRNA references.
Tested periscope and periscope_multi on simulated and biological sequencing datasets.

Main Results:

The periscope tool demonstrated biased predictions and high computational costs.
The redesigned algorithm, periscope_multi, showed enhanced accuracy in detecting both canonical and non-canonical sgRNAs.
sgENERATE effectively assessed the performance of sgRNA detection tools.

Conclusions:

There is a need for specialized computational tools for viral sgRNA detection.
periscope_multi offers a more accurate and efficient approach for analyzing viral sgRNA from sequencing data.
This work advances the field of viral RNA discovery and analysis.