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...

Sanger Sequencing

Sanger Sequencing

DNA sequencing is a fundamental technique that is routinely used in the biological sciences. This method can be applied to a range of questions at different scales - from the sequencing of a cloned DNA fragment or the study of a mutation in a gene up to whole-genome sequencing. However, despite the widespread use of sequencing today, it was not until 1977 that Fredrick Sanger and his collaborators developed the chain-termination method to decode DNA sequences. It relies on the separation of a...

You might also read

Related Articles

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

Sort by

Same author

Respiratory syncytial viral load drives ciliated cell dedifferentiation and suppresses antiviral immunity.

Science advances·2026

Same author

Single-cell RNA-seq using UltraMarathonRT expands the known transcriptome.

bioRxiv : the preprint server for biology·2025

Same author

mRNA turnover dynamics are affected by cell differentiation and loss of the cytosine methyltransferase Nsun2.

Nucleic acids research·2025

Same author

Phenotypic screens identify SCAF1 as critical activator of RNAPII elongation and global transcription.

Nucleic acids research·2024

Same author

Cell-type-specific mRNA transcription and degradation kinetics in zebrafish embryogenesis from metabolically labeled single-cell RNA-seq.

Nature communications·2024

Same author

Human cytomegalovirus exploits STING signaling and counteracts IFN/ISG induction to facilitate infection of dendritic cells.

Nature communications·2024

Same journal

Demonstration of a quantum C-NOT gate in a time-multiplexed fully reconfigurable photonic processor.

Nature communications·2026

Same journal

Nonlinear quantum light source with van der Waals ferroelectric NbOX<sub>2</sub> (X = Br, I).

Nature communications·2026

Same journal

Antagonistic histone H2A variants and autonomous heterochromatin formation shape epigenomic patterns in Arabidopsis.

Nature communications·2026

Same journal

The long tail of nitrate pollution in groundwater challenges governance of global water quality.

Nature communications·2026

Same journal

Select microbial metabolites promote tau aggregation in a murine tauopathy model.

Nature communications·2026

Same journal

Warming climate has lengthened global intense tropical cyclone seasons.

Nature communications·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Jul 26, 2025

Author Spotlight: Investigating the Role of Repetitive DNA Misregulation in Cancer Initiation and Immunotherapy Resistance

Author Spotlight: Investigating the Role of Repetitive DNA Misregulation in Cancer Initiation and Immunotherapy Resistance

Published on: December 13, 2024

grandR: a comprehensive package for nucleotide conversion RNA-seq data analysis.

Teresa Rummel¹, Lygeri Sakellaridi¹, Florian Erhard^2,3

¹Institute for Virology and Immunobiology, University of Würzburg, Versbacher Str. 7, 97078, Würzburg, Germany.

Nature Communications

|June 15, 2023

Summary

This summary is machine-generated.

This study introduces grandR, a package for analyzing metabolic RNA labeling data. It offers tools for quality control, differential gene expression, and kinetic modeling to understand gene expression dynamics.

More Related Videos

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

A Bioinformatics Pipeline for Investigating Molecular Evolution and Gene Expression using RNA-seq

A Bioinformatics Pipeline for Investigating Molecular Evolution and Gene Expression using RNA-seq

Published on: May 28, 2021

Related Experiment Videos

Last Updated: Jul 26, 2025

Author Spotlight: Investigating the Role of Repetitive DNA Misregulation in Cancer Initiation and Immunotherapy Resistance

Author Spotlight: Investigating the Role of Repetitive DNA Misregulation in Cancer Initiation and Immunotherapy Resistance

Published on: December 13, 2024

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

A Bioinformatics Pipeline for Investigating Molecular Evolution and Gene Expression using RNA-seq

A Bioinformatics Pipeline for Investigating Molecular Evolution and Gene Expression using RNA-seq

Published on: May 28, 2021

Area of Science:

Molecular Biology
Bioinformatics
Systems Biology

Background:

Metabolic labeling of RNA is crucial for studying gene expression dynamics.
Nucleotide conversion methods generate valuable data but pose analytical challenges.

Purpose of the Study:

To present grandR, a comprehensive R package for analyzing metabolic RNA labeling data.
To improve the analysis of RNA synthesis rates and half-lives from time-course experiments.
To introduce a Bayesian approach for studying RNA temporal dynamics.

Main Methods:

Development of the grandR R package for data analysis.
Comparison of existing methods for inferring RNA synthesis rates and half-lives.
Implementation of a Bayesian framework for temporal RNA dynamics analysis.

Main Results:

grandR provides tools for quality control, differential gene expression, kinetic modeling, and visualization.
Recalibration of effective labeling times is necessary for accurate analysis.
A Bayesian approach enables robust study of RNA temporal dynamics.

Conclusions:

grandR offers a unified solution for analyzing metabolic RNA labeling data.
Accurate analysis requires recalibration of labeling times and advanced modeling.
The package facilitates deeper insights into gene expression regulation.