Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Global Regulatory Systems01:28

Global Regulatory Systems

Global regulatory systems in bacteria enable rapid and coordinated responses to environmental changes by integrating sensory inputs with gene expression, ensuring efficient adaptation to fluctuating conditions. Key global regulatory mechanisms include regulons, two-component systems, sigma factors, and secondary messengers.Regulons and Global RegulatorsA regulon is a collection of genes and operons controlled by a common global regulator. These regulators enable bacteria to prioritize resource...

You might also read

Related Articles

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

Sort by
Same author

A novel maternal prenatal risk index to predict mortality-weighted severe maternal morbidity at hospitalization: a retrospective cohort study.

Lancet regional health. Americas·2026
Same author

Information-Based Composite Likelihood Method for Hybrid Meta-Analysis Integrating Individual Participant Data and Aggregated Data.

Statistics in medicine·2026
Same author

Canopy2: Tumor Phylogeny Inference by Bulk DNA and Single-Cell RNA Sequencing.

Statistics in biosciences·2026
Same author

Surgical operation duration as a predictor of venous thromboembolism risk after radical cystectomy.

Urologic oncology·2026
Same author

Mortality-weighted severe maternal morbidity: a novel approach to assessing maternal health outcomes.

BMC pregnancy and childbirth·2025
Same author

Pair-Feeding Study Designs Can Create Biases and Inflate Type I Error Rates: A Simulation Study.

Obesity (Silver Spring, Md.)·2025
Same journal

OpenIMC: an open-source platform for analyzing single-cell and spatial proteomics by imaging mass cytometry.

BMC bioinformatics·2026
Same journal

NAP: an open source pipeline for cross-domain microbiome profiling using Nanopore sequencing-derived amplicon data.

BMC bioinformatics·2026
Same journal

SurvGME: an R package for survival analysis with graphical and measurement error models.

BMC bioinformatics·2026
Same journal

SimMapNet: a Bayesian framework for gene regulatory network inference using gene ontology similarities as external hint.

BMC bioinformatics·2026
Same journal

Dual channel drug-drug interactions extraction based on cross attention.

BMC bioinformatics·2026
Same journal

FeSseqdb: a curated sequence-level database and interpretable machine learning framework for identifying iron-sulfur proteins.

BMC bioinformatics·2026
See all related articles

Related Experiment Video

Updated: May 8, 2026

High-Throughput Transcriptome Analysis for Investigating Host-Pathogen Interactions
14:58

High-Throughput Transcriptome Analysis for Investigating Host-Pathogen Interactions

Published on: March 5, 2022

Differential expression analysis with global network adjustment.

Jonathan A Gelfond1, Joseph G Ibrahim, Mayetri Gupta

  • 1Department of Epidemiology and Biostatistics, University of Texas Health Science Center San Antonio, San Antonio, Texas, USA. gelfondjal@uthscsa.edu.

BMC Bioinformatics
|August 24, 2013
PubMed
Summary
This summary is machine-generated.

This study introduces a novel penalized regression model to predict gene expression, improving differential expression analysis by accounting for global regulatory networks. The method enhances signal-to-noise ratio for more powerful and reliable biological findings.

More Related Videos

Three Differential Expression Analysis Methods for RNA Sequencing: limma, EdgeR, DESeq2
10:10

Three Differential Expression Analysis Methods for RNA Sequencing: limma, EdgeR, DESeq2

Published on: September 18, 2021

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline
10:44

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline

Published on: December 7, 2021

Related Experiment Videos

Last Updated: May 8, 2026

High-Throughput Transcriptome Analysis for Investigating Host-Pathogen Interactions
14:58

High-Throughput Transcriptome Analysis for Investigating Host-Pathogen Interactions

Published on: March 5, 2022

Three Differential Expression Analysis Methods for RNA Sequencing: limma, EdgeR, DESeq2
10:10

Three Differential Expression Analysis Methods for RNA Sequencing: limma, EdgeR, DESeq2

Published on: September 18, 2021

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline
10:44

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline

Published on: December 7, 2021

Area of Science:

  • Genomics
  • Systems Biology
  • Bioinformatics

Background:

  • Global transcriptomic perturbations affect numerous genes, necessitating methods to identify key affected genes.
  • Transcriptional regulation confounds differential gene expression analysis, especially in smaller datasets.
  • Large gene expression databases can inform models of transcriptional regulation for smaller experiments.

Purpose of the Study:

  • To develop a method for predicting gene expression as a function of other genes, accounting for global regulatory effects.
  • To identify genes most profoundly affected by large-scale chromosomal deletions or transcriptome perturbations.
  • To improve the identification of differentially expressed genes in the presence of complex regulatory networks.

Main Methods:

  • A penalized regression model is estimated using large gene expression databases.
  • Ridge parameter selection is optimized via cross-validation error minimization.
  • A computationally efficient "over-shrinkage" method is proposed, outperforming LASSO-based techniques.

Main Results:

  • The model explains approximately 25% of gene expression variability across two independent datasets.
  • A substantial increase in signal-to-noise ratio enables more powerful differential gene expression inferences.
  • Conditional gene dependencies on biological state are identified, unachievable with standard methods.

Conclusions:

  • Adjusting for global network effects significantly enhances the sensitivity and reliability of differential expression measures.
  • The proposed method provides biologically intuitive findings by improving differential gene expression analysis.
  • This approach offers a more powerful tool for understanding gene regulation and its impact on biological states.