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

RNA-seq

RNA-seq

RNA sequencing, or RNA-Seq, is a high-throughput sequencing technology used to study the transcriptome of a cell. Transcriptomics helps to interpret the functional elements of a genome and identify the molecular constituents of an organism. Additionally, it also helps in understanding the development of an organism and the occurrence of diseases.
Before the discovery of RNA-seq, microarray-based methods and Sanger sequencing were used for transcriptome analysis. However, while...

You might also read

Related Articles

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

Sort by

Same author

Reduced gravity and muon flux absence affect <i>Caenorhabditis elegans</i> life history traits and viral infection.

Microbiology spectrum·2026

Same author

Experimental evolution of the host range dynamics in two isolates of potato virus Y.

Virus evolution·2026

Same author

Latent infection of Caenorhabditis elegans by Orsay virus induces age-dependent immunity and cross-protection.

Nature communications·2025

Same author

Taylor's Power Law rules the dynamics of allele frequencies during viral evolution in response to host changes.

Journal of the Royal Society, Interface·2025

Same author

Almond Grafting for Plum Pox Virus Resistance Triggers Significant Transcriptomic and Epigenetic Shifts in Peaches.

International journal of molecular sciences·2025

Same author

Population dynamics of defective viral genomes of tomato black ring virus during host-to-host transmission.

Journal of virology·2024

Search research articles

Related Experiment Video

Updated: Jun 8, 2025

Isolation of Fidelity Variants of RNA Viruses and Characterization of Virus Mutation Frequency

Isolation of Fidelity Variants of RNA Viruses and Characterization of Virus Mutation Frequency

Published on: June 16, 2011

Quantifying defective and wild-type viruses from high-throughput RNA sequencing.

Juan C Muñoz-Sánchez^1,2, María J Olmo-Uceda¹, José-Ángel Oteo^1,2

¹Institute for Integrative Systems Biology (I2SysBio), CSIC-Universitat de València, Paterna, València 46980, Spain.

Bioinformatics (Oxford, England)

|November 4, 2024

Summary

This summary is machine-generated.

This study introduces a novel method for quantifying defective viral genomes (DVGs) alongside wild-type (wt) viruses. The new approach accurately estimates DVG and wt genome proportions from RNA sequencing data, crucial for understanding viral evolution and infection.

More Related Videos

Unbiased Deep Sequencing of RNA Viruses from Clinical Samples

Unbiased Deep Sequencing of RNA Viruses from Clinical Samples

Published on: July 2, 2016

Reverse Genetics to Engineer Positive-Sense RNA Virus Variants

Reverse Genetics to Engineer Positive-Sense RNA Virus Variants

Published on: June 9, 2022

Related Experiment Videos

Last Updated: Jun 8, 2025

Isolation of Fidelity Variants of RNA Viruses and Characterization of Virus Mutation Frequency

Isolation of Fidelity Variants of RNA Viruses and Characterization of Virus Mutation Frequency

Published on: June 16, 2011

Unbiased Deep Sequencing of RNA Viruses from Clinical Samples

Unbiased Deep Sequencing of RNA Viruses from Clinical Samples

Published on: July 2, 2016

Reverse Genetics to Engineer Positive-Sense RNA Virus Variants

Reverse Genetics to Engineer Positive-Sense RNA Virus Variants

Published on: June 9, 2022

Area of Science:

Virology
Genomics
Bioinformatics

Background:

Defective viral genomes (DVGs) are viral variants that require a helper wild-type (wt) virus for replication.
DVGs influence viral evolution, host immune responses, and infection outcomes, making their quantification critical.
Accurate quantification of DVGs is challenging due to their dependence on wt viruses and difficulties in distinguishing them in sequencing data.

Purpose of the Study:

To develop a method for simultaneously estimating the abundances of DVGs and wt genomes.
To enable genome-wide quantification of DVG and wt proportions from short-read RNA sequencing data.

Main Methods:

Developed a computational method to estimate viral genome abundances by identifying deviations in sequencing depth.
Utilized a linear system of equations to reconstruct depth profiles and quantify wt and DVG genomes.
The method provides genome-wide estimates, unlike previous region-specific approaches.

Main Results:

Successfully developed a method for simultaneous estimation of wt and DVG genome abundances.
The approach accurately quantifies proportions of both genome types from RNA sequencing data.
This is the first method to provide genome-wide simultaneous quantification of wt and DVGs.

Conclusions:

The developed method offers a significant advancement in quantifying DVGs and wt viruses.
Accurate quantification is essential for understanding DVG dynamics and their impact on viral infections.
The freely available code and datasets facilitate further research in this area.