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

22 Comparison of conventional and controlled-rate freezing methods to cryopreserve white spotted bamboo shark (<i>Chiloscyllium plagiosum</i>) sperm: implications for elasmobranch biobanking efforts.

Reproduction, fertility, and development·2022

Same author

Biomechanical comparison of tenorrhaphy constructs for tendon reconstructions and transfers.

Hand surgery & rehabilitation·2021

Same author

Positive and negative forms of replicability in gene network analysis.

Bioinformatics (Oxford, England)·2015

Same author

A randomized, placebo-controlled, double-blind study of sapropterin to treat ADHD symptoms and executive function impairment in children and adults with sapropterin-responsive phenylketonuria.

Molecular genetics and metabolism·2014

Same author

Measuring the wisdom of the crowds in network-based gene function inference.

Bioinformatics (Oxford, England)·2014

Same author

De novo mutations in schizophrenia implicate chromatin remodeling and support a genetic overlap with autism and intellectual disability.

Molecular psychiatry·2014

Search research articles

Related Experiment Video

Updated: Apr 16, 2026

Analyzing Multifactorial RNA-Seq Experiments with DiCoExpress

Analyzing Multifactorial RNA-Seq Experiments with DiCoExpress

Published on: July 29, 2022

Guidance for RNA-seq co-expression network construction and analysis: safety in numbers.

S Ballouz¹, W Verleyen¹, J Gillis¹

¹Stanley Institute for Cognitive Genomics, Cold Spring Harbor Laboratory, 500 Sunnyside Boulevard Woodbury, NY 11797, USA.

Bioinformatics (Oxford, England)

|February 27, 2015

Summary

RNA-sequencing (RNA-seq) co-expression network analysis requires thousands of samples for gold-standard results. RNA-seq networks show topological differences compared to microarrays due to expression noise correlations.

More Related Videos

High-Throughput Transcriptome Analysis for Investigating Host-Pathogen Interactions

High-Throughput Transcriptome Analysis for Investigating Host-Pathogen Interactions

Published on: March 5, 2022

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 16, 2026

Analyzing Multifactorial RNA-Seq Experiments with DiCoExpress

Analyzing Multifactorial RNA-Seq Experiments with DiCoExpress

Published on: July 29, 2022

High-Throughput Transcriptome Analysis for Investigating Host-Pathogen Interactions

High-Throughput Transcriptome Analysis for Investigating Host-Pathogen Interactions

Published on: March 5, 2022

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:

Bioinformatics
Computational Biology
Systems Biology

Background:

RNA-sequencing (RNA-seq) co-expression network analysis is an emerging field with undefined best practices.
Understanding factors influencing functional connectivity and network topology in RNA-seq data is crucial.

Purpose of the Study:

To assess RNA-seq expression data to identify factors impacting co-expression network properties.
To compare RNA-seq co-expression networks with those derived from microarrays.

Main Methods:

Utilized a Guilt-By-Association framework on 1970 RNA-seq samples.
Examined gene co-expression tendencies to infer shared function.
Evaluated network performance using the area under the receiver operator characteristic curve.

Main Results:

Minimal criteria for RNA-seq network performance comparable to microarrays include >20 samples with >10M reads/sample.
Aggregate RNA-seq networks achieved an AUC of ~0.71.
Thousands of samples are needed for 'gold-standard' co-expression, similar to microarrays.
Significant topological differences exist between RNA-seq and microarray networks, particularly in hub gene overlap, due to technology-specific expression noise correlations.

Conclusions:

RNA-seq co-expression network analysis requires substantial sample sizes for robust results.
Differences in expression noise between RNA-seq and microarrays lead to distinct network topologies.
Further research is needed to refine practices for RNA-seq co-expression network construction.