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

Regulation of Expression Occurs at Multiple Steps02:24

Regulation of Expression Occurs at Multiple Steps

Gene expression can be regulated at almost every step from gene to protein. Transcription is the step that is most commonly regulated. This involves the binding of proteins to short regulatory sequences on the DNA. This association can either promote or inhibit the transcription of a gene associated with the respective sequence.
Transcription results in the generation of precursor (pre-mRNA) that consists of both exons and introns, which needs further processing before being translated to a...
Regulation of Expression Occurs at Multiple Steps02:24

Regulation of Expression Occurs at Multiple Steps

Gene expression can be regulated at almost every step from gene to protein. Transcription is the step that is most commonly regulated. This involves the binding of proteins to short regulatory sequences on the DNA. This association can either promote or inhibit the transcription of a gene associated with the respective sequence.
Transcription results in the generation of precursor (pre-mRNA) that consists of both exons and introns, which needs further processing before being translated to a...
Regulation of Expression at Multiple Steps01:23

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 addition of a...
Cis-regulatory Sequences02:02

Cis-regulatory Sequences

Cis-regulatory sequences are short fragments of non-coding DNA that are present on the same chromosomes as the genes that they regulate. These fragments serve as binding sites for transcriptional regulators, proteins that are responsible for controlling gene transcription and differential gene expression across cell types in eukaryotes. Cis-regulatory sequences can be close to the gene of interest or thousands of bases away in the DNA sequence; however, those sequences that are further away are...
Cis-regulatory Sequences02:02

Cis-regulatory Sequences

Cis-regulatory sequences are short fragments of non-coding DNA that are present on the same chromosomes as the genes that they regulate. These fragments serve as binding sites for transcriptional regulators, proteins that are responsible for controlling gene transcription and differential gene expression across cell types in eukaryotes. Cis-regulatory sequences can be close to the gene of interest or thousands of bases away in the DNA sequence; however, those sequences that are further away are...
Combinatorial Gene Control02:33

Combinatorial Gene Control

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...

You might also read

Related Articles

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

Sort by
Same author

PhyloCNN: Improving Tree Representation and Neural Network Architecture for Deep Learning from Trees in Phylodynamics and Diversification Studies.

Systematic biology·2025
Same author

Accelerating Maximum Likelihood Phylogenetic Inference via Early Stopping to Evade (Over-)optimization.

Systematic biology·2025
Same author

Accounting for contact tracing in epidemiological birth-death models.

PLoS computational biology·2025
Same author

Tumor antigens preferentially derive from unmutated genomic sequences in melanoma and non-small cell lung cancer.

Nature cancer·2025
Same author

The NF1 tumor suppressor regulates PD-L1 and immune evasion in melanoma.

Cell reports·2025
Same author

multistrap: boosting phylogenetic analyses with structural information.

Nature communications·2025

Related Experiment Video

Updated: May 16, 2026

Using SCOPE to Identify Potential Regulatory Motifs in Coregulated Genes
07:55

Using SCOPE to Identify Potential Regulatory Motifs in Coregulated Genes

Published on: May 31, 2011

Computational discovery of regulatory elements in a continuous expression space.

Mathieu Lajoie, Olivier Gascuel, Vincent Lefort

    Genome Biology
    |November 29, 2012
    PubMed
    Summary

    This study introduces RED2, a novel method for discovering gene regulatory elements. RED2 bypasses data clustering to identify motifs, revealing more characterized regulatory motifs than traditional methods.

    More Related Videos

    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

    High-throughput Identification of Gene Regulatory Sequences Using Next-generation Sequencing of Circular Chromosome Conformation Capture (4C-seq)
    09:06

    High-throughput Identification of Gene Regulatory Sequences Using Next-generation Sequencing of Circular Chromosome Conformation Capture (4C-seq)

    Published on: October 5, 2018

    Related Experiment Videos

    Last Updated: May 16, 2026

    Using SCOPE to Identify Potential Regulatory Motifs in Coregulated Genes
    07:55

    Using SCOPE to Identify Potential Regulatory Motifs in Coregulated Genes

    Published on: May 31, 2011

    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

    High-throughput Identification of Gene Regulatory Sequences Using Next-generation Sequencing of Circular Chromosome Conformation Capture (4C-seq)
    09:06

    High-throughput Identification of Gene Regulatory Sequences Using Next-generation Sequencing of Circular Chromosome Conformation Capture (4C-seq)

    Published on: October 5, 2018

    Area of Science:

    • Genomics
    • Bioinformatics
    • Computational Biology

    Background:

    • Gene expression data analysis often uses clustering to group co-expressed genes for regulatory motif discovery.
    • Clustering-based partitioning may not accurately reflect biological complexity, potentially missing regulatory elements.

    Purpose of the Study:

    • To develop and present a novel method, RED2, for regulatory element discovery that avoids data clustering.
    • To demonstrate RED2's ability to identify motifs missed by conventional clustering approaches.

    Main Methods:

    • RED2 estimates motif densities locally around individual genes, bypassing the need for pre-defined clusters.
    • The method analyzes gene expression data to identify overrepresented sequence motifs.

    Main Results:

    • RED2 successfully detected numerous motifs that were not identified by clustering-based methods.
    • A significant proportion of the newly detected motifs were confirmed as characterized regulatory motifs.
    • The RED2 method is accessible via a user-friendly online interface.

    Conclusions:

    • RED2 offers a more effective approach to regulatory element discovery compared to clustering-based methods.
    • By avoiding ad hoc data partitioning, RED2 improves the detection of biologically relevant regulatory motifs.