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 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...
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...
Reporter Genes02:11

Reporter Genes

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.
Commonly used reporter...
mRNA Stability and Gene Expression02:51

mRNA Stability and Gene Expression

The structure and stability of mRNA molecules regulates gene expression, as mRNAs are a key step in the pathway from gene to protein. In eukaryotes, the half-life of mRNA varies from a few minutes up to several days. mRNA stability is essential in growth and development. The absence of the proteins regulating its stability, such as tristetraprolin in mice, can cause systemic issues, including bone marrow overgrowth, inflammation, and autoimmunity.
Cis-acting Elements involved in mRNA stability
mRNA Stability and Gene Expression02:51

mRNA Stability and Gene Expression

The structure and stability of mRNA molecules regulates gene expression, as mRNAs are a key step in the pathway from gene to protein. In eukaryotes, the half-life of mRNA varies from a few minutes up to several days. mRNA stability is essential in growth and development. The absence of the proteins regulating its stability, such as tristetraprolin in mice, can cause systemic issues, including bone marrow overgrowth, inflammation, and autoimmunity.
Cis-acting Elements involved in mRNA stability

You might also read

Related Articles

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

Sort by
Same author

SAA1 Promotes Pro-inflammatory Macrophage-mediated Bone Invasion in Silent Corticotroph Adenomas.

Genomics, proteomics & bioinformatics·2026
Same author

From pharmacometric foundations to emerging artificial intelligence applications: A bibliometric analysis of model-informed precision dosing for anti-infective therapy (2005-2025).

Digital health·2026
Same author

Implementing Patient Decision Aids for Insulin Initiation in China: What are the Barriers and Facilitators? A TDF-Based Qualitative Study.

Journal of diabetes research·2026
Same author

Personalized Dosing System Guided by Cross-Scale Metabolic Imaging Biomarkers in Neovascular Age-Related Macular Degeneration.

Translational vision science & technology·2026
Same author

What do cancer patients discuss online regarding CINV management? A social media-based topic modeling study.

Frontiers in oncology·2026
Same author

Widespread marine and freshwater distributions of active sulfoquinovose-degrading bacteria.

The ISME journal·2026

Related Experiment Video

Updated: Jun 13, 2026

Generating the Transcriptional Regulation View of Transcriptomic Features for Prediction Task and Dark Biomarker Detection on Small Datasets
03:37

Generating the Transcriptional Regulation View of Transcriptomic Features for Prediction Task and Dark Biomarker Detection on Small Datasets

Published on: March 1, 2024

Learning "graph-mer" motifs that predict gene expression trajectories in development.

Xuejing Li1, Casandra Panea, Chris H Wiggins

  • 1Department of Physics, Columbia University, New York, New York, United States of America.

Plos Computational Biology
|May 11, 2010
PubMed
Summary

This study introduces a new computational framework to predict gene expression from DNA sequences, identifying novel regulatory motifs in Caenorhabditis elegans development. The method effectively maps sequence information to gene expression patterns, advancing our understanding of transcriptional regulation.

More Related Videos

Droplet Barcoding-Based Single Cell Transcriptomics of Adult Mammalian Tissues
10:12

Droplet Barcoding-Based Single Cell Transcriptomics of Adult Mammalian Tissues

Published on: January 10, 2019

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: Jun 13, 2026

Generating the Transcriptional Regulation View of Transcriptomic Features for Prediction Task and Dark Biomarker Detection on Small Datasets
03:37

Generating the Transcriptional Regulation View of Transcriptomic Features for Prediction Task and Dark Biomarker Detection on Small Datasets

Published on: March 1, 2024

Droplet Barcoding-Based Single Cell Transcriptomics of Adult Mammalian Tissues
10:12

Droplet Barcoding-Based Single Cell Transcriptomics of Adult Mammalian Tissues

Published on: January 10, 2019

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
  • Computational Biology
  • Developmental Biology

Background:

  • Understanding transcriptional regulatory networks requires deciphering cis regulatory logic in gene promoter sequences.
  • Current computational methods often rely on gene clustering, which can be challenging with highly correlated expression data, like developmental time series.
  • Genes with similar expression profiles may be governed by distinct regulatory programs.

Purpose of the Study:

  • To develop a predictive framework for modeling the flow of information from promoter sequence to gene expression.
  • To learn cis regulatory motifs and characterize gene expression patterns in developmental time courses using a cluster-free approach.
  • To apply this framework to wildtype germline development in Caenorhabditis elegans.

Main Methods:

  • Introduced a cluster-free algorithm based on graph-regularized partial least squares (PLS) regression.
  • Represented sequence patterns as graphs of k-mers, termed "graph-mers".
  • Applied the model to developmental time course expression data from Caenorhabditis elegans.

Main Results:

  • The first and second latent PLS factors successfully mapped to expression profiles for oocyte and sperm genes, respectively.
  • Identified both known and novel motifs, including CG-rich motifs specific to oocyte genes, from associated graph-mers.
  • Provided evidence for the functional relevance of identified motifs through positional bias, motif conservation, and in situ gene expression analysis.

Conclusions:

  • The developed regression model can learn biologically meaningful latent structure from subtle developmental time course expression data.
  • The framework successfully identifies potentially functional cis regulatory motifs.
  • This approach enhances the understanding of transcriptional regulatory networks and gene expression patterns during development.