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

Combinatorial Gene Control02:33

Combinatorial Gene Control

8.2K
Combinatorial gene control is the synergistic action of several transcriptional factors to regulate the expression of a single gene. The absence of one or more of these factors may lead to a significant difference in the level of gene expression or repression.
The expression of more than 30,000 genes is controlled by approximately 2000-3000 transcription factors. This is possible because a single transcription factor can recognize more than one regulatory sequence. The specificity in gene...
8.2K
Co-activators and Co-repressors02:04

Co-activators and Co-repressors

7.1K
Gene transcription is regulated by the synergistic action of several proteins that form a complex at a gene regulatory site. This is observed in eukaryotes, where the regulation of gene expression is a complex process. Regulatory proteins in eukaryotes can broadly be classified into two types – regulators that bind directly to specific DNA sequences and co-regulators that associate with regulatory proteins but cannot directly bind to the DNA. These co-regulators are further divided into...
7.1K
General Transcription Factors01:30

General Transcription Factors

5.1K
Tissue-specific transcription factors contribute to diverse cellular functions in mammals. For example, the gene for beta globin, a major component of hemoglobin, is present in all cells of the body. However, it is only expressed in red blood cells because the transcription factors that can bind to the promoter sequences of the beta globin gene are only expressed in these cells. Tissue-specific transcription factors also ensure that mutations in these factors may impair only the function of...
5.1K
Reporter Genes02:11

Reporter Genes

11.0K
Reporter genes are a type of protein-coding gene that are often tagged to a gene of interest. Once inside a target cell, reporter genes usually produce visually identifiable characteristics like fluorescence and luminescence when expressed along with the gene of interest. Thus, reporter genes “report” the presence or absence of genes of interest in an organism, determine the gene expression pattern, or track the physical location of a DNA segment or protein in the cell.
11.0K
Cooperative Binding of Transcription Regulators02:13

Cooperative Binding of Transcription Regulators

6.2K
Transcriptional regulators bind to specific cis-regulatory sequences in the DNA to regulate gene transcription. These cis-regulatory sequences are very short, usually less than ten nucleotide pairs in length. The short length means that there is a high probability of the exact same sequence randomly occurring throughout the genome.  Since regulators can also bind to groups of similar sequences, this further increases the chances of random binding. Transcriptional regulators form...
6.2K
Regulation of Expression at Multiple Steps01:23

Regulation of Expression at Multiple Steps

845
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...
845

You might also read

Related Articles

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

Sort by
Same author

Evaluation of analysis modes for RNA coexpression in single-cell and bulk tissue.

bioRxiv : the preprint server for biology·2026
Same author

Application of large language models to the annotation of cell lines and mouse strains in genomics data.

bioRxiv : the preprint server for biology·2026
Same author

Thiorphan reprograms neurons to promote functional recovery after spinal cord injury.

Nature·2025
Same author

Global partnerships in rare disease research.

Disease models & mechanisms·2025
Same author

Revealing function-altering MECP2 mutations in individuals with autism spectrum disorder using yeast and Drosophila.

Genetics·2025
Same author

Identifying Reproducible Transcription Regulator Coexpression Patterns with Single Cell Transcriptomics.

bioRxiv : the preprint server for biology·2024
Same journal

Another 10 years of PLOS Computational Biology: A data-driven reflection on trends in genomics research.

PLoS computational biology·2026
Same journal

Mobility data resolution needed to inform predictive models of spatial epidemic spread from mobile phone data.

PLoS computational biology·2026
Same journal

DeepMethylation: A deep learning framework for tissue-specific DNA methylation prediction and functional variant annotation.

PLoS computational biology·2026
Same journal

Redefining and estimating the early-phase reproduction ratio for epidemic outbreaks in spatially structured populations.

PLoS computational biology·2026
Same journal

Optimized phenotype definitions boost GWAS power.

PLoS computational biology·2026
Same journal

Detection, communication, and individual identification with deep audio embeddings: A case study with North Atlantic right whales.

PLoS computational biology·2026
See all related articles

Related Experiment Video

Updated: May 10, 2025

A Combinatorial Single-cell Approach to Characterize the Molecular and Immunophenotypic Heterogeneity of Human Stem and Progenitor Populations
09:34

A Combinatorial Single-cell Approach to Characterize the Molecular and Immunophenotypic Heterogeneity of Human Stem and Progenitor Populations

Published on: October 25, 2018

6.6K

Identifying reproducible transcription regulator coexpression patterns with single cell transcriptomics.

Alexander Morin1,2,3, Ching Pan Chu1,2,3, Paul Pavlidis1,2

  • 1Michael Smith Laboratories, University of British Columbia, Vancouver, British Columbia, Canada.

Plos Computational Biology
|April 21, 2025
PubMed
Summary
This summary is machine-generated.

This study maps gene coexpression networks across millions of cells to identify gene partners of human and mouse transcription regulators (TRs). The findings reveal reproducible regulatory patterns, aiding in understanding gene transcription dynamics.

More Related Videos

Transcriptome Analysis of Single Cells
07:27

Transcriptome Analysis of Single Cells

Published on: April 25, 2011

29.7K
Real-time Analysis of Transcription Factor Binding, Transcription, Translation, and Turnover to Display Global Events During Cellular Activation
12:54

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

Published on: March 7, 2018

13.4K

Related Experiment Videos

Last Updated: May 10, 2025

A Combinatorial Single-cell Approach to Characterize the Molecular and Immunophenotypic Heterogeneity of Human Stem and Progenitor Populations
09:34

A Combinatorial Single-cell Approach to Characterize the Molecular and Immunophenotypic Heterogeneity of Human Stem and Progenitor Populations

Published on: October 25, 2018

6.6K
Transcriptome Analysis of Single Cells
07:27

Transcriptome Analysis of Single Cells

Published on: April 25, 2011

29.7K
Real-time Analysis of Transcription Factor Binding, Transcription, Translation, and Turnover to Display Global Events During Cellular Activation
12:54

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

Published on: March 7, 2018

13.4K

Area of Science:

  • Genomics
  • Computational Biology
  • Molecular Biology

Background:

  • Single cell transcriptomics enables detailed analysis of gene transcription regulation.
  • Understanding transcription regulator (TR) networks is crucial for deciphering cellular processes.

Purpose of the Study:

  • To identify gene partners coordinated with human and mouse transcription regulators (TRs) using large-scale single cell RNA-seq data.
  • To assess the consistency of TR coexpression profiles across diverse biological contexts and species.

Main Methods:

  • Assembled 120 human and 103 mouse single cell RNA-seq datasets (>28 million cells).
  • Constructed single cell coexpression networks for human and mouse TRs.
  • Prioritized reproducible coexpression patterns across cell types and species.

Main Results:

  • Generated single cell coexpression rankings for each TR, validating against literature-curated targets.
  • Achieved performance comparable to ChIP-seq data in identifying TR targets.
  • Integrated coexpression and ChIP-seq data to identify robust candidate regulatory interactions.

Conclusions:

  • The study provides a comprehensive resource for understanding transcription regulator networks.
  • Identified reproducible gene coexpression patterns across species and cell types.
  • Highlights the utility of integrated data for discovering novel regulatory interactions, exemplified by ASCL1.