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...

Regulation of Expression at Multiple Steps

Regulation of Expression at Multiple Steps

The gene expression in cells is regulated at different stages: (i) transcription, (ii) RNA processing, (iii) RNA localization, and (iv) translation. Transcriptional regulation is mediated by regulatory proteins such as transcription factors, activators, or repressors—these control gene expression by initiating or inhibiting the transcription of genes. Once a precursor or pre-mRNA is produced, it undergoes post-transcriptional modification, including 5' capping, splicing, and the...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

Long-term spatiotemporal evolution of drug-resistant Mycobacterium tuberculosis in China.

Nature communications·2026

Same author

Granuloma dual RNA-seq reveals composite transcriptional programs driven by neutrophils and necrosis within tuberculous granulomas.

Science advances·2026

Same author

Clinical testing of drug treatment shortening in patients with TB using PET/CT imaging of lung lesions.

Science translational medicine·2026

Same author

Origin and adaptive evolutionary trajectory of the 3' UTR-derived sRNA UhpU in Enterobacteriaceae.

Proceedings of the National Academy of Sciences of the United States of America·2025

Same author

Immunopeptidomics can inform the design of mRNA vaccines for the delivery of <i>Mycobacterium tuberculosis</i> MHC class II antigens.

Science translational medicine·2025

Same author

A host-directed adjuvant sensitizes intracellular bacterial persisters to antibiotics.

Nature microbiology·2025

Same journal

SA-MTP: a structure-aware framework for multifunctional therapeutic peptide annotation.

Briefings in bioinformatics·2026

Same journal

Genome assemblies and annotations are not static and need support for tracking their evolution.

Briefings in bioinformatics·2026

Same journal

A historical journey of metabolite-protein interaction discovery: from data harmonization to AI-driven prediction.

Briefings in bioinformatics·2026

Same journal

Bridging local-global transmembrane protein contexts with contrastive pretraining for alignment-free pathogenicity prediction.

Briefings in bioinformatics·2026

Same journal

Prediction of drug hypersensitivity by comprehensive modeling of HLA-peptidomes.

Briefings in bioinformatics·2026

Same journal

EssTFNet: integration of adaptive time-frequency and DNA language models for interpretable human essential gene prediction.

Briefings in bioinformatics·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Jan 14, 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

A transcriptional plasticity-aware framework for RNA-seq differential expression analysis.

Cheng Bei¹, Xiaoman Wang², Mingyu Gan³

¹Key Laboratory of Medical Molecular Virology (MOE/NHC/CAMS), School of Basic Medical Science, Shanghai Medical College, Shanghai Institute of Infectious Disease and Biosecurity, Fudan University, 138 Yixueyuan Rd, Xuhui District, Shanghai 200032, China.

Briefings in Bioinformatics

|October 20, 2025

Summary

This summary is machine-generated.

Transcriptional plasticity (TP) biases gene expression analysis. A new TP-aware framework adjusts fold changes, improving differential expression (DE) results and revealing novel biological insights in bacteria.

Keywords:

RNA-seq bacteria differential expression analysis mycobacterium transcriptional plasticity

More Related Videos

Identification of Alternative Splicing and Polyadenylation in RNA-seq Data

Identification of Alternative Splicing and Polyadenylation in RNA-seq Data

Published on: June 24, 2021

Real-time Analysis of Transcription Factor Binding, Transcription, Translation, and Turnover to Display Global Events During Cellular Activation

Real-time Analysis of Transcription Factor Binding, Transcription, Translation, and Turnover to Display Global Events During Cellular Activation

Published on: March 7, 2018

Related Experiment Videos

Last Updated: Jan 14, 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

Identification of Alternative Splicing and Polyadenylation in RNA-seq Data

Identification of Alternative Splicing and Polyadenylation in RNA-seq Data

Published on: June 24, 2021

Real-time Analysis of Transcription Factor Binding, Transcription, Translation, and Turnover to Display Global Events During Cellular Activation

Real-time Analysis of Transcription Factor Binding, Transcription, Translation, and Turnover to Display Global Events During Cellular Activation

Published on: March 7, 2018

Area of Science:

Microbiology
Genomics
Bioinformatics

Background:

Differential expression (DE) analysis of transcriptomic data assesses genome-wide gene responsiveness.
Transcriptional plasticity (TP), the variability of gene expression with environmental changes, has not been explored for its impact on DE analysis.

Purpose of the Study:

To investigate the impact of TP on DE analysis.
To introduce a TP-aware framework to enhance the interpretation of DE results.

Main Methods:

Correlated gene expression fold change with TP across 238 experiments in Mycobacterium tuberculosis (Mtb) and Escherichia coli (E. coli).
Applied Locally Estimated Scatterplot Smoothing to TP to adjust gene expression fold changes.
Performed adjusted DE analyses to identify responsive pathways.

Main Results:

Identified inherent biases in DE analysis favoring high-TP genes and overlooking low-TP genes.
Adjusted DE analyses revealed new responsive pathways with higher statistical significance and enrichment scores, particularly for low-TP genes.
Specific findings include Mtb response to bedaquiline (cholesterol degradation), linezolid (acetate metabolism repression), and macrophage infection (fatty acid metabolism upregulation, cofactor biosynthesis downregulation).

Conclusions:

The TP-aware framework normalizes DE analysis by correcting for inherent transcriptional variability.
This adjustment strategy is applicable to diverse bacterial species and compatible with various RNA-seq quantification methods.
The approach improves the discovery of biologically relevant gene expression changes, especially those involving genes with low transcriptional plasticity.