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

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

Loss of METTL14 in dopaminergic neurons disrupts ER homeostasis via m6A-dependent regulation of Atp2a3 mRNA: Implications for Parkinson's Disease.

NPJ Parkinson's disease·2026

Same author

Nucleosome context regulates chromatin reader preference.

Nucleic acids research·2025

Same author

Multivalent binding of the tardigrade Dsup protein to chromatin promotes yeast survival and longevity upon exposure to oxidative damage.

Nature communications·2025

Same author

Physiologically Based Modeling of Cefoxitin, Cefazolin, and Piperacillin Pharmacokinetics in Obese and Lean Rats.

The AAPS journal·2025

Same author

RNA Editing Analysis Reveals Methyl Jasmonic Acid Regulation of Fucoxanthin and Fatty Acid Metabolism in <i>Phaeodactylum tricornutum</i>.

Marine drugs·2025

Same author

Pharmacokinetics of Nivolumab and Erythropoietin in a Rat Model of Diet-Induced Obesity.

Pharmaceutical research·2025

Search research articles

Related Experiment Video

Updated: Dec 14, 2025

Identification of Key Factors Regulating Self-renewal and Differentiation in EML Hematopoietic Precursor Cells by RNA-sequencing Analysis

Identification of Key Factors Regulating Self-renewal and Differentiation in EML Hematopoietic Precursor Cells by RNA-sequencing Analysis

Published on: November 11, 2014

NetExtractor: Extracting a Cerebellar Tissue Gene Regulatory Network Using Differentially Expressed High Mutual

Benafsh Husain¹, Allison R Hickman², Yuqing Hang²

¹Biomedical Data Science and Informatics Program, Clemson University, Clemson, SC.

G3 (Bethesda, Md.)

|July 16, 2020

Summary

Our NetExtractor algorithm identifies complex, non-linear gene expression relationships missed by traditional methods. It constructs novel gene co-expression networks (GCNs) for brain tissue, revealing biologically relevant connections.

Keywords:

Cerebellar gene Differential RNA Mutual Information expression regulatory network

More Related Videos

Analyzing Multifactorial RNA-Seq Experiments with DiCoExpress

Analyzing Multifactorial RNA-Seq Experiments with DiCoExpress

Published on: July 29, 2022

RNA-Seq Analysis of Differential Gene Expression in Electroporated Chick Embryonic Spinal Cord

RNA-Seq Analysis of Differential Gene Expression in Electroporated Chick Embryonic Spinal Cord

Published on: November 1, 2014

Related Experiment Videos

Last Updated: Dec 14, 2025

Identification of Key Factors Regulating Self-renewal and Differentiation in EML Hematopoietic Precursor Cells by RNA-sequencing Analysis

Identification of Key Factors Regulating Self-renewal and Differentiation in EML Hematopoietic Precursor Cells by RNA-sequencing Analysis

Published on: November 11, 2014

Analyzing Multifactorial RNA-Seq Experiments with DiCoExpress

Analyzing Multifactorial RNA-Seq Experiments with DiCoExpress

Published on: July 29, 2022

RNA-Seq Analysis of Differential Gene Expression in Electroporated Chick Embryonic Spinal Cord

RNA-Seq Analysis of Differential Gene Expression in Electroporated Chick Embryonic Spinal Cord

Published on: November 1, 2014

Area of Science:

Genomics
Computational Biology
Systems Biology

Background:

Traditional gene co-expression network (GCN) construction relies on linear correlations (Pearson, Spearman), failing to capture non-linear dependencies.
Noise and sub-population structures in gene expression data obscure biologically relevant gene relationships.
Existing methods struggle to identify complex, latent bigenic expression patterns.

Purpose of the Study:

To develop and validate a novel algorithm, NetExtractor, for detecting non-linear, differential bigenic expression relationships.
To overcome limitations of conventional correlation-based methods in GCN construction.
To identify novel gene expression networks in brain tissue.

Main Methods:

NetExtractor employs Gaussian mixture models (GMMs) to identify sample sub-populations.
Mutual information (MI) analysis is used to detect non-linear bigenic expression relationships.
Application to Genotype-Tissue Expression (GTEx) brain RNA profiles and integration with PsychENCODE gene regulatory network (GRN) data.

Main Results:

NetExtractor successfully identified non-linear gene expression relationships in brain tissue.
A brain tissue-specific gene expression relationship network was constructed, enriched for cerebellar and cerebellar hemisphere edges.
A cerebellar cortex (cerebellar) gene regulatory network (GRN) was generated, linked to transcriptionally active regions.

Conclusions:

NetExtractor effectively detects biologically relevant and novel non-linear binary gene relationships.
The algorithm enhances the construction of gene co-expression networks by accounting for data complexity.
This approach provides a powerful tool for uncovering intricate gene interactions in complex tissues like the brain.