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...

Ribosome Profiling

Ribosome Profiling

Ribosome profiling or ribo-sequencing is a deep sequencing technique that produces a snapshot of active translation in a cell. It selectively sequences the mRNAs protected by ribosomes to get an insight into a cell’s translation landscape at any given point in time.
Applications of ribosome profiling
Ribosome profiling has many applications, including in vivo monitoring of translation inside a particular organ or tissue type and quantifying new protein synthesis levels.
The technique...

You might also read

Related Articles

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

Sort by

Same author

A two-step auxin-GA cross talk regulates organ formation.

Development (Cambridge, England)·2026

Same author

Polyadenylation of insulin mRNA by Tent5a regulates pancreatic beta cells.

Nature communications·2026

Same author

Distinct dendritic cell cytoskeletal programs dictate synapse architecture and CD8<sup>+</sup> T cell fate.

Frontiers in immunology·2026

Same author

CUT&Tag for High-Resolution Epigenomic Profiling From a Low Amount of <i>Arabidopsis</i> Tissue.

Plant direct·2026

Same author

Genome-wide transcriptomic analysis of Toxoplasma gondii reveals stage-specific regulatory programs and metabolic adaptations driving oocyst sporulation.

BMC genomics·2026

Same author

Evaluation of Therapeutic Opioids in Skin-Derived Matrices (Sweat and Sebum) of Neonatal and Pediatric Patients and Their Role in Opioid Incorporation Into Hair.

Drug testing and analysis·2026

Same journal

Mapping the 3D Chromosome Organization of a Biosynthetic Gene Cluster by Capture Hi-C (CHi-C).

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

Same journal

Mapping the 3D Chromosome Organization of Streptomyces by Hi-C.

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

Same journal

CUT&Tag Epigenomic Profiling of Biosynthetic Gene Clusters in Arabidopsis thaliana.

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

Same journal

Rhizobium rhizogenes-Mediated Hairy Root Transformation Protocol for Lotus japonicus and Other Legumes.

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

Same journal

Characterization of Bioactive Saponins from Sea Cucumbers.

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

Same journal

Methods for Functional Validation of Terpenoid Metabolic Clusters in Nicotiana benthamiana and Aspergillus oryzae.

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

See all related articles

Search research articles

Related Experiment Video

Updated: Feb 22, 2026

Author Spotlight: AQRNA-seq Role in Mapping Small RNAs and Unraveling Protein Translation Mechanisms

Author Spotlight: AQRNA-seq Role in Mapping Small RNAs and Unraveling Protein Translation Mechanisms

Published on: February 2, 2024

RNA-Seq Data Analysis Protocol: Combining In-House and Publicly Available Data.

Marc W Schmid^1,2,3,4

¹Department of Evolutionary Biology and Environmental Studies, University of Zurich, Winterthurerstrasse 190, 8057, Zürich, Switzerland. schmid.m@access.uzh.ch.

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

|September 23, 2017

Summary

This summary is machine-generated.

This study presents a unified workflow for processing and comparing RNA-Seq gene expression data from diverse sources. The method ensures identical data processing for robust analysis of cell differentiation and environmental responses.

Keywords:

Analysis Data integration Differential expression Gene expression Multigroup comparisons Public data RNA-Seq Transcriptome Workflow

More Related Videos

Targeted Next-generation Sequencing and Bioinformatics Pipeline to Evaluate Genetic Determinants of Constitutional Disease

Targeted Next-generation Sequencing and Bioinformatics Pipeline to Evaluate Genetic Determinants of Constitutional Disease

Published on: April 4, 2018

Targeted RNA Sequencing Assay to Characterize Gene Expression and Genomic Alterations

Targeted RNA Sequencing Assay to Characterize Gene Expression and Genomic Alterations

Published on: August 4, 2016

Related Experiment Videos

Last Updated: Feb 22, 2026

Author Spotlight: AQRNA-seq Role in Mapping Small RNAs and Unraveling Protein Translation Mechanisms

Author Spotlight: AQRNA-seq Role in Mapping Small RNAs and Unraveling Protein Translation Mechanisms

Published on: February 2, 2024

Targeted Next-generation Sequencing and Bioinformatics Pipeline to Evaluate Genetic Determinants of Constitutional Disease

Targeted Next-generation Sequencing and Bioinformatics Pipeline to Evaluate Genetic Determinants of Constitutional Disease

Published on: April 4, 2018

Targeted RNA Sequencing Assay to Characterize Gene Expression and Genomic Alterations

Targeted RNA Sequencing Assay to Characterize Gene Expression and Genomic Alterations

Published on: August 4, 2016

Area of Science:

Genomics
Bioinformatics
Molecular Biology

Background:

Comparing gene expression profiles across different tissues, developmental stages, or conditions is crucial for understanding cell specification and environmental responses.
Consistent data processing is essential for accurate comparisons and integration of diverse RNA-Seq datasets.
Existing methods may lack a unified approach for comprehensive RNA-Seq data analysis and integration.

Purpose of the Study:

To describe a complete and standardized workflow for RNA-Seq data processing and gene expression profile comparison.
To provide a method for integrating novel datasets with existing collections, including public and private data.
To offer a versatile workflow applicable to various research scenarios in genomics and molecular biology.

Main Methods:

Development of a comprehensive workflow for RNA-Seq data analysis, covering all processing steps.
Utilization of publicly available RNA-Seq datasets for demonstration and validation.
Description of procedures for integrating user-specific datasets into the workflow.
Ensuring the workflow's compatibility with major operating systems (Linux, MacOS, Windows).

Main Results:

A complete RNA-Seq data processing and comparison workflow has been established.
The workflow facilitates the integration of diverse datasets, enhancing comparative analyses.
Demonstration using public data confirms the workflow's efficacy in analyzing gene expression profiles.
The protocol is accessible and applicable across different computational environments.

Conclusions:

The presented workflow provides a standardized and robust method for RNA-Seq data analysis and comparison.
This approach enables deeper insights into cell differentiation and tissue-specific responses.
The accessibility and cross-platform compatibility facilitate wider adoption in biological research.
The protocol supports the integration of diverse data sources for more comprehensive genomic studies.