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

Cryo-EM structure and functional analysis of the chromatin remodeler RSF.

Acta crystallographica. Section F, Structural biology communications·2024

Same author

Effect of Heat Treatment Combined with TG Enzyme Cross-Linking on the Zein-Pea Protein Complex: Physicochemical and Gel Properties.

Gels (Basel, Switzerland)·2024

Same author

Perfluorooctanesulfonic acid exposure leads to downregulation of 3-hydroxy-3-methylglutaryl-CoA synthase 2 expression and upregulation of markers associated with intestinal carcinogenesis in mouse intestinal tissues.

Chemosphere·2024

Same author

Overexpression of Fatty Acid Synthase Upregulates Glutamine-Fructose-6-Phosphate Transaminase 1 and O-Linked N-Acetylglucosamine Transferase to Increase O-GlcNAc Protein Glycosylation and Promote Colorectal Cancer Growth.

International journal of molecular sciences·2024

Same author

Factors associated with patients' healthcare-seeking behavior and related clinical outcomes under China's hierarchical healthcare delivery system.

Frontiers in public health·2024

Same author

Combined effect of time in target range and variability of systolic blood pressure on cardiovascular outcomes and mortality in patients with hypertension: A prospective cohort study.

Journal of clinical hypertension (Greenwich, Conn.)·2024

Same journal

MCFST: Spatial domain identification method based on multi-view graph convolutional network and graph fusion network.

Bioinformatics (Oxford, England)·2026

Same journal

SpaBiT: Enhancing Spatial Transcriptomics Resolution via Bidirectional Attention Transformers.

Bioinformatics (Oxford, England)·2026

Same journal

EDEL: Enhancing Dense Retrievers for Curation of Biomedical Knowledge Bases.

Bioinformatics (Oxford, England)·2026

Same journal

Informative Relational Learning for Adverse Reaction Prediction with Enhanced Generalization to Novel Drugs.

Bioinformatics (Oxford, England)·2026

Same journal

An interpretable deep learning framework uncovers features governing CRISPR-Cas9 genome-editing efficiency.

Bioinformatics (Oxford, England)·2026

Same journal

3DICE: Interpretable 3D Cross-Modal Learning for Drug-Target Interaction Prediction and Large-Scale Drug Discovery.

Bioinformatics (Oxford, England)·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Apr 23, 2026

Three Differential Expression Analysis Methods for RNA Sequencing: limma, EdgeR, DESeq2

Three Differential Expression Analysis Methods for RNA Sequencing: limma, EdgeR, DESeq2

Published on: September 18, 2021

PROPER: comprehensive power evaluation for differential expression using RNA-seq.

Hao Wu¹, Chi Wang¹, Zhijin Wu¹

¹Department of Biostatistics and Bioinformatics, Emory University, Atlanta, GA 30322, Department of Biostatistics and Markey Cancer Center, University of Kentucky, Lexington, KY 40536 and Department of Biostatistics, Brown University, Providence, RI 02806, USA.

Bioinformatics (Oxford, England)

|October 3, 2014

Summary

This summary is machine-generated.

This study introduces PROPER, a new R software package for prospective power assessment in RNA-sequencing experiments. It aids researchers in optimizing experimental design, including sample size and sequencing depth, for accurate differential expression analysis.

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

Rup (RNA-seq Usability Assessment Pipeline) - Quality Control for Bulk RNA-seq Experiments in Eukaryotes

Rup (RNA-seq Usability Assessment Pipeline) - Quality Control for Bulk RNA-seq Experiments in Eukaryotes

Published on: November 7, 2025

Related Experiment Videos

Last Updated: Apr 23, 2026

Three Differential Expression Analysis Methods for RNA Sequencing: limma, EdgeR, DESeq2

Three Differential Expression Analysis Methods for RNA Sequencing: limma, EdgeR, DESeq2

Published on: September 18, 2021

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

Rup (RNA-seq Usability Assessment Pipeline) - Quality Control for Bulk RNA-seq Experiments in Eukaryotes

Rup (RNA-seq Usability Assessment Pipeline) - Quality Control for Bulk RNA-seq Experiments in Eukaryotes

Published on: November 7, 2025

Area of Science:

Genomics
Bioinformatics
Statistical Genetics

Background:

RNA-sequencing (RNA-seq) is crucial for differential expression (DE) analysis.
Determining optimal sample size for RNA-seq experiments is challenging due to data complexity and influencing factors.
Accurate power assessment is vital for robust DE detection.

Purpose of the Study:

To develop a prospective power assessment method for RNA-seq experiments.
To provide a tool that assists in experimental design decisions, such as sample size and sequencing depth.
To enhance the reliability of differential expression identification in transcriptomic studies.

Main Methods:

A semi-parametric simulation approach generates realistic RNA-seq data based on actual experiments.
The tool allows flexible parameter choices for baseline expression, biological variation, and DE patterns.
Introduces concepts of stratified power and false discovery cost for comprehensive power evaluation.

Main Results:

The proposed method enables prospective power assessment, moving beyond direct sample size calculation.
Demonstrates the utility of the tool in optimizing experimental design parameters like sample size and sequencing depth.
Highlights the importance of gene filtering strategies in the analysis plan.

Conclusions:

The PROPER R package offers a valuable resource for researchers conducting RNA-seq studies.
Facilitates informed experimental design for improved power in differential expression analysis.
Contributes to more reliable and reproducible transcriptomic research.