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

Protein Networks

Protein Networks

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

scSurvival: Single-Cell Survival Analysis of Clinical Cancer Cohort Data at Cellular Resolution.

Cancer discovery·2026

Same author

Advancing spatial cellular communication inference with ligand diffusion and transport model.

Communications biology·2026

Same author

Enhancing Spatial Transcriptomics via Spatially Constrained Matrix Decomposition with EDGES.

Advanced science (Weinheim, Baden-Wurttemberg, Germany)·2025

Same author

Flexible integration of spatial and expression information for precise spot embedding via ZINB-based graph-enhanced autoencoder.

Communications biology·2025

Same author

Reverse network diffusion to remove indirect noise for better inference of gene regulatory networks.

Bioinformatics (Oxford, England)·2024

Same author

Incorporating network diffusion and peak location information for better single-cell ATAC-seq data analysis.

Briefings in bioinformatics·2024

Same journal

K-attention: a biologically informed attention operator for data-efficient sequence-based omics modeling.

Briefings in bioinformatics·2026

Same journal

Accurate prediction of asparagine deamidation in biologics using advanced machine learning models.

Briefings in bioinformatics·2026

Same journal

scImmuneCo: a compendium of cell-type-specific functional modules for decoding immune responses from single-cell RNA-seq data.

Briefings in bioinformatics·2026

Same journal

scGenoByte: a GenoByte embedding transformer with biological priors for cell type annotation.

Briefings in bioinformatics·2026

Same journal

FerroScore: a statistical approach for quantifying tumor-related ferroptosis based on omics data.

Briefings in bioinformatics·2026

Same journal

METEOR: a data-adaptive Mendelian randomization method for powerful detection of shared and specific exposures underlying multiple outcomes.

Briefings in bioinformatics·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Aug 24, 2025

Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers

Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers

Published on: March 1, 2024

Interaction-based transcriptome analysis via differential network inference.

Jiacheng Leng^1,2, Ling-Yun Wu^1,2

¹IAM, MADIS, NCMIS, Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing 100190, China.

Briefings in Bioinformatics

|October 24, 2022

Summary

This summary is machine-generated.

This study introduces interaction-based transcriptome analysis (IBTA) for a deeper understanding of gene interactions in biological processes. It presents a new Co-hub Differential Network (CDN) algorithm and metric to reveal disease mechanisms more effectively.

Keywords:

differential expression analysis differential network inference interaction-based transcriptome analysis

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

JUMPn: A Streamlined Application for Protein Co-Expression Clustering and Network Analysis in Proteomics

JUMPn: A Streamlined Application for Protein Co-Expression Clustering and Network Analysis in Proteomics

Published on: October 19, 2021

Related Experiment Videos

Last Updated: Aug 24, 2025

Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers

Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers

Published on: March 1, 2024

High-Throughput Transcriptome Analysis for Investigating Host-Pathogen Interactions

High-Throughput Transcriptome Analysis for Investigating Host-Pathogen Interactions

Published on: March 5, 2022

JUMPn: A Streamlined Application for Protein Co-Expression Clustering and Network Analysis in Proteomics

JUMPn: A Streamlined Application for Protein Co-Expression Clustering and Network Analysis in Proteomics

Published on: October 19, 2021

Area of Science:

Bioinformatics
Systems Biology
Genomics

Background:

Gene-based transcriptome analysis, like differential expression, identifies key factors but overlooks crucial gene interactions.
Current differential network inference methods often rely solely on node information, limiting downstream analysis.
Understanding gene interactions is vital for comprehending complex biological processes and disease mechanisms.

Purpose of the Study:

To propose and validate interaction-based transcriptome analysis (IBTA) as a superior approach to gene-based analysis.
To introduce a novel Co-hub Differential Network (CDN) inference algorithm and an interaction-based metric, pivot APC2.
To demonstrate the utility of IBTA in uncovering causative mechanisms for diseases like cancer and COVID-19.

Main Methods:

Development of the Co-hub Differential Network (CDN) algorithm for inferring differential gene interactions.
Introduction of a new interaction-based metric, pivot APC2, for analyzing differential networks.
Validation of CDN performance through simulation experiments against existing methods.
Application of the IBTA workflow to real-world datasets from colorectal cancer, COVID-19, and triple-negative breast cancer.

Main Results:

The CDN algorithm demonstrated superior performance in differential network inference compared to popular existing algorithms.
The developed IBTA workflow, utilizing CDN and pivot APC2, effectively analyzes differential gene interactions.
Case studies revealed the capability of the interaction-based approach to uncover underlying disease mechanisms.

Conclusions:

Interaction-based transcriptome analysis (IBTA) provides deeper biological insights than traditional gene-based methods.
The CDN algorithm and pivot APC2 metric represent significant advancements in differential gene network analysis.
This IBTA framework holds promise for identifying novel causative mechanisms in various diseases.