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...

Genome Annotation and Assembly

Genome Annotation and Assembly

The genome refers to all of the genetic material in an organism. It can range from a few million base pairs in microbial cells to several billion base pairs in many eukaryotic organisms. Genome assembly refers to the process of taking the DNA sequencing data and putting it all back together in a correct order to create a close representation of the original genome. This is followed by the identification of functional elements on the newly assembled genome, a process called genome annotation.

You might also read

Related Articles

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

Sort by

Same author

Infection History Shapes Co-Epidemic Dynamics: A Transmission Source-Pathway Decomposition for COVID-19 and Influenza.

Microorganisms·2026

Same author

Addressing convergence in the emerging era of structural phylogenetics.

Frontiers in molecular biosciences·2026

Same author

CRISPRi-based functional genomic screening identifies genes essential for CH<sub>4</sub>-dependent growth in a methanotrophic bacterium.

Science advances·2026

Same author

Gene regulatory and biomolecular networks and their multifaceted biotechnological applications.

World journal of microbiology & biotechnology·2026

Same author

bHLH35 mediates specificity in plant responses to multiple stress conditions.

Science advances·2025

Same author

Comprehensive multifaceted gene network analysis towards understanding multi-metal and oxidative stress responses in Synechocystis PCC 6803.

Computers in biology and medicine·2025

Same journal

Tracking Synthetic Adhesins on Bacterial Surfaces with Immunofluorescence Microscopy.

Methods in molecular biology (Clifton, N.J.)·2026

Same journal

Post-Selection Methods for Analyzing mRNA Display Selections and Optimization of Hits.

Methods in molecular biology (Clifton, N.J.)·2026

Same journal

High-Performance Computing in Tandem Mass Spectrometry (MS/MS) Peptide Identification.

Methods in molecular biology (Clifton, N.J.)·2026

Same journal

Engineering and Adapting Disulfide-Containing Proteins to Enable Intracellular Functionality.

Methods in molecular biology (Clifton, N.J.)·2026

Same journal

AI-Driven Protein Research: From Prediction to Design.

Methods in molecular biology (Clifton, N.J.)·2026

Same journal

Methods for the In Vitro Selection of Protein and Peptide Libraries Using mRNA Display.

Methods in molecular biology (Clifton, N.J.)·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Oct 13, 2025

Isolation and Transcriptome Analysis of Plant Cell Types

Isolation and Transcriptome Analysis of Plant Cell Types

Published on: April 7, 2023

RNA-Seq Data Analysis Pipeline for Plants: Transcriptome Assembly, Alignment, and Differential Expression Analysis.

David J Burks¹, Rajeev K Azad^2,3

¹Department of Biological Sciences and BioDiscovery Institute, University of North Texas, Denton, TX, USA.

Methods in Molecular Biology (Clifton, N.J.)

|November 17, 2021

Summary

This summary is machine-generated.

This chapter details a pipeline for RNA sequencing (RNA-Seq) data analysis, from raw reads to identifying differentially expressed genes and their functions. It covers de novo transcriptome assembly and gene quantification for robust biological insights.

Keywords:

Alignment Differential expression analysis RNA-Seq data analysis Transcriptome assembly Transcriptomics

More Related Videos

A Bioinformatics Pipeline to Accurately and Efficiently Analyze the MicroRNA Transcriptomes in Plants

A Bioinformatics Pipeline to Accurately and Efficiently Analyze the MicroRNA Transcriptomes in Plants

Published on: January 21, 2020

An Experimental and Bioinformatics Protocol for RNA-seq Analyses of Photoperiodic Diapause in the Asian Tiger Mosquito, Aedes albopictus

An Experimental and Bioinformatics Protocol for RNA-seq Analyses of Photoperiodic Diapause in the Asian Tiger Mosquito, Aedes albopictus

Published on: November 30, 2014

Related Experiment Videos

Last Updated: Oct 13, 2025

Isolation and Transcriptome Analysis of Plant Cell Types

Isolation and Transcriptome Analysis of Plant Cell Types

Published on: April 7, 2023

A Bioinformatics Pipeline to Accurately and Efficiently Analyze the MicroRNA Transcriptomes in Plants

A Bioinformatics Pipeline to Accurately and Efficiently Analyze the MicroRNA Transcriptomes in Plants

Published on: January 21, 2020

An Experimental and Bioinformatics Protocol for RNA-seq Analyses of Photoperiodic Diapause in the Asian Tiger Mosquito, Aedes albopictus

An Experimental and Bioinformatics Protocol for RNA-seq Analyses of Photoperiodic Diapause in the Asian Tiger Mosquito, Aedes albopictus

Published on: November 30, 2014

Area of Science:

Bioinformatics
Computational Biology
Genomics

Background:

RNA sequencing (RNA-Seq) is a powerful technology for transcriptomic analysis.
Analyzing RNA-Seq data requires a structured pipeline from raw reads to biological interpretation.
De novo transcriptome assembly is crucial for organisms lacking reference genomes.

Purpose of the Study:

To present a comprehensive pipeline for RNA-Seq data analysis.
To guide researchers through de novo assembly, quantification, and differential expression analysis.
To provide accessible tools and data for RNA-Seq analysis.

Main Methods:

Quality assessment and preprocessing of raw RNA-Seq reads.
De novo transcriptome assembly for non-model organisms.
Gene and transcript-level quantification.
Differential gene expression analysis between experimental conditions.
Functional annotation using tools like InterProScan and Blast2Go.

Main Results:

A complete workflow for RNA-Seq data analysis is described.
Methods for de novo assembly, quantification, and differential expression are detailed.
Functional characterization of differentially expressed genes is achieved.

Conclusions:

The described pipeline enables robust RNA-Seq data analysis, from raw data to functional insights.
The provided resources, including sample data and scripts, facilitate downstream analyses.
This approach supports transcriptomic studies in both reference-based and de novo contexts.