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

Protocadherin-1 deficiency increases baseline and allergen-induced airway hyperresponsiveness in mice.

American journal of respiratory cell and molecular biology·2026

Same author

Heterogeneity and dynamics of DENV-specific CD8 + T cells in dengue infection.

Nature communications·2026

Same author

A Hormone Cell Atlas maps the human endocrine system at cellular resolution.

Science (New York, N.Y.)·2026

Same author

Physicochemical stability and bactericidal efficacy of a novel premixed triple antibiotic paste during storage: an in vitro study.

BMC oral health·2026

Same author

An integrated single-cell and spatial proteotranscriptomics atlas of fibroblast-driven immunoregulation within the human adult oral cavity.

Cell press blue·2026

Same author

Decoding Human T Cell Immunity with Artificial Intelligence and Single-Cell Genomics.

Annual review of immunology·2026

Search research articles

Related Experiment Video

Updated: Jul 25, 2025

A Combinatorial Single-cell Approach to Characterize the Molecular and Immunophenotypic Heterogeneity of Human Stem and Progenitor Populations

A Combinatorial Single-cell Approach to Characterize the Molecular and Immunophenotypic Heterogeneity of Human Stem and Progenitor Populations

Published on: October 25, 2018

Computational workflow for investigating highly variable genes in single-cell RNA-seq across multiple time points and

Jantarika Kumar Arora¹, Anunya Opasawatchai², Sarah A Teichmann³

¹Doctor of Philosophy Program in Biochemistry (International Program), Faculty of Science, Mahidol University, Bangkok 10400, Thailand; Department of Biochemistry, Faculty of Science, Mahidol University, Bangkok 10400, Thailand.

|June 28, 2023

Summary

This study introduces a computational method to analyze highly variable genes (HVGs) in single-cell RNA sequencing data. It helps understand gene expression dynamics in biological pathways across different immune cells and time points during viral infections.

Keywords:

Bioinformatics Gene Expression Immunology RNAseq Single Cell Systems Biology

More Related Videos

Droplet Barcoding-Based Single Cell Transcriptomics of Adult Mammalian Tissues

Droplet Barcoding-Based Single Cell Transcriptomics of Adult Mammalian Tissues

Published on: January 10, 2019

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 25, 2025

A Combinatorial Single-cell Approach to Characterize the Molecular and Immunophenotypic Heterogeneity of Human Stem and Progenitor Populations

A Combinatorial Single-cell Approach to Characterize the Molecular and Immunophenotypic Heterogeneity of Human Stem and Progenitor Populations

Published on: October 25, 2018

Droplet Barcoding-Based Single Cell Transcriptomics of Adult Mammalian Tissues

Droplet Barcoding-Based Single Cell Transcriptomics of Adult Mammalian Tissues

Published on: January 10, 2019

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:

Computational biology
Immunology
Genomics

Background:

Single-cell RNA sequencing (scRNA-seq) generates complex datasets for studying cellular heterogeneity.
Identifying highly variable genes (HVGs) is crucial for understanding cellular responses.
Investigating dynamic gene expression in biological pathways requires robust analytical frameworks.

Purpose of the Study:

To present a computational framework for analyzing HVGs linked to biological pathways in scRNA-seq data.
To characterize dynamic gene expression patterns of HVGs across multiple time points and cell types.
To apply the framework to public dengue virus and COVID-19 datasets.

Main Methods:

Development of a computational approach for HVG analysis in scRNA-seq data.
Application of the framework to analyze gene expression dynamics in immune cells.
Utilizing public datasets from dengue virus and COVID-19 studies.

Main Results:

The framework successfully characterizes dynamic expression levels of HVGs.
It identifies common and cell-type-specific biological pathways associated with HVGs.
Demonstrated utility in analyzing viral infection datasets.

Conclusions:

The presented computational approach provides a valuable tool for dissecting gene expression variability in scRNA-seq data.
It facilitates the understanding of immune responses to viral infections at a systems level.
The method aids in identifying key genes and pathways involved in host-pathogen interactions.