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...

Next-generation Sequencing

Next-generation Sequencing

The first human genome sequencing project cost $2.7 billion and was declared complete in 2003, after 15 years of international cooperation and collaboration between several research teams and funding agencies. Today, with the advent of next-generation sequencing technologies, the cost and time of sequencing a human genome have dropped over 100 fold.
Next-Generation Sequencing Methods
Although all next-generation methods use different technologies, they all share a set of standard features....

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

Environmental enrichment is associated with enhanced novel object recognition performance and an increased proportion of smooth endoplasmic reticulum-containing spines in the dentate gyrus of Septin3<sup>-/-</sup> mice.

Molecular brain·2026

Same author

Comparison of area under the concentration-time curve estimations between Practical Antimicrobial Therapeutic Drug Monitoring (PAT) versions 3 and 4 for vancomycin using two-point sampling.

Journal of infection and chemotherapy : official journal of the Japan Society of Chemotherapy·2026

Same author

Exploring the influence of sex on <i>idiosyncratic</i> DILI: impact on risk assessment.

Expert opinion on drug metabolism & toxicology·2026

Same author

Association of Statin Use with Reduced Primary Liver Cancer Risk, Independent of Age and Cirrhosis Protection in MASLD.

Cancers·2026

Same author

Development of a risk prediction model for vancomycin-associated acute kidney injury: a multicentre retrospective study.

The Journal of antimicrobial chemotherapy·2026

Same author

Age and sex influence transcriptomic responses to statin therapy that control liver aging during systemic metabolic dysfunction.

Hepatology (Baltimore, Md.)·2026

Search research articles

Related Experiment Video

Updated: Apr 28, 2026

Improving Small RNA-seq: Less Bias and Better Detection of 2'-O-Methyl RNAs

Improving Small RNA-seq: Less Bias and Better Detection of 2'-O-Methyl RNAs

Published on: September 16, 2019

RNA sequencing: from sample preparation to analysis.

Sho Maekawa¹, Ayako Suzuki, Sumio Sugano

¹Department of Medical Genome Sciences, Graduate School of Frontier Sciences, The University of Tokyo, 5-1-5 Kashiwanoha, Kashiwa-shi, Chiba, 277-8562, Japan.

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

|June 15, 2014

Summary

RNA sequencing (RNA-seq) offers accurate transcriptomics analysis by quantifying all RNA and isoforms. This guide details RNA-seq sample preparation and bioinformatic analysis for researchers.

More Related Videos

Optimization for Sequencing and Analysis of Degraded FFPE-RNA Samples

Optimization for Sequencing and Analysis of Degraded FFPE-RNA Samples

Published on: June 8, 2020

Next-generation Sequencing of 16S Ribosomal RNA Gene Amplicons

Next-generation Sequencing of 16S Ribosomal RNA Gene Amplicons

Published on: August 29, 2014

Related Experiment Videos

Last Updated: Apr 28, 2026

Improving Small RNA-seq: Less Bias and Better Detection of 2'-O-Methyl RNAs

Improving Small RNA-seq: Less Bias and Better Detection of 2'-O-Methyl RNAs

Published on: September 16, 2019

Optimization for Sequencing and Analysis of Degraded FFPE-RNA Samples

Optimization for Sequencing and Analysis of Degraded FFPE-RNA Samples

Published on: June 8, 2020

Next-generation Sequencing of 16S Ribosomal RNA Gene Amplicons

Next-generation Sequencing of 16S Ribosomal RNA Gene Amplicons

Published on: August 29, 2014

Area of Science:

Molecular Biology
Genomics
Bioinformatics

Background:

Next-generation sequencing technologies have driven advancements in transcriptomics.
RNA sequencing (RNA-seq) provides high-accuracy quantification of cellular RNA and its isoforms.

Purpose of the Study:

To provide a comprehensive, step-by-step guide for RNA sequencing sample preparation.
To offer an overview of the essential bioinformatic analysis pipeline for RNA-seq data.

Main Methods:

Detailed protocol for RNA isolation and library preparation for RNA-seq.
Description of computational steps for RNA-seq data processing and analysis.

Main Results:

A standardized workflow for reproducible RNA-seq experiments.
Guidelines for interpreting transcriptomic data obtained from RNA-seq.

Conclusions:

RNA-seq is a powerful tool for transcriptomic studies, enabling detailed analysis of gene expression.
This guide facilitates the effective implementation and analysis of RNA-seq experiments in research settings.