Search research articles

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

Risk-calibrated sharing of human brain data.

Brain : a journal of neurology·2026

Same author

UNLOCKING MULTI-SAMPLE DIFFERENTIAL EXPRESSION FOR SPATIAL TRANSCRIPTOMICS DATA WITH TESSERA.

bioRxiv : the preprint server for biology·2026

Same author

Inventing the future: A neuroscience research roadmap.

Neuron·2026

Same author

CMDdemux: an efficient single cell demultiplexing method.

Nucleic acids research·2026

Same author

TNF-⍺-mediated myeloid-instructed CD14<sup>+</sup>CD4<sup>+</sup> T cells are associated with poor survival in lung adenocarcinoma.

Cell reports. Medicine·2026

Same author

A latent activated olfactory stem cell state revealed by single-cell transcriptomic and epigenomic profiling.

Stem cell reports·2026

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

Search research articles

Related Experiment Video

Updated: Apr 25, 2026

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

Normalization of RNA-seq data using factor analysis of control genes or samples.

Davide Risso¹, John Ngai², Terence P Speed³

¹Department of Statistics, University of California, Berkeley, Berkeley, California, USA.

Nature Biotechnology

|August 25, 2014

Summary

Standard RNA-sequencing (RNA-seq) normalization methods often miss technical biases. A new strategy, remove unwanted variation (RUV), uses control genes or samples to correct these effects for more accurate gene expression analysis.

More Related Videos

Author Spotlight: Investigating the Role of Repetitive DNA Misregulation in Cancer Initiation and Immunotherapy Resistance

Author Spotlight: Investigating the Role of Repetitive DNA Misregulation in Cancer Initiation and Immunotherapy Resistance

Published on: December 13, 2024

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

Related Experiment Videos

Last Updated: Apr 25, 2026

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

Author Spotlight: Investigating the Role of Repetitive DNA Misregulation in Cancer Initiation and Immunotherapy Resistance

Author Spotlight: Investigating the Role of Repetitive DNA Misregulation in Cancer Initiation and Immunotherapy Resistance

Published on: December 13, 2024

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

Area of Science:

Genomics
Bioinformatics
Computational Biology

Background:

Accurate RNA-sequencing (RNA-seq) data analysis relies on effective normalization to account for technical variations.
Current normalization methods primarily address sequencing depth but often fail to correct for complex library preparation and other unwanted technical effects.

Purpose of the Study:

To evaluate the reliability of External RNA Control Consortium (ERCC) spike-in controls for RNA-seq normalization.
To develop and propose a novel normalization strategy to address limitations in existing RNA-seq data processing.

Main Methods:

Assessed the performance of ERCC spike-in controls in standard normalization procedures.
Developed and applied the remove unwanted variation (RUV) method, utilizing factor analysis on control genes or samples.
Compared RUV performance against state-of-the-art normalization techniques.

Main Results:

ERCC spike-ins were found to be unreliable for standard global-scaling or regression-based normalization.
The RUV approach effectively adjusts for nuisance technical effects in RNA-seq data.
RUV demonstrated improved accuracy in estimating expression fold-changes and conducting differential expression tests compared to existing methods.

Conclusions:

The RUV normalization strategy offers a more robust approach to RNA-seq data analysis by correcting for complex technical variations.
RUV is particularly beneficial for large-scale, multi-laboratory, or multi-platform RNA-seq projects.
This method enhances the reliability and accuracy of gene expression inference from RNA-seq data.