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

1.4K
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...
1.4K
Ribosome Profiling02:24

Ribosome Profiling

4.2K
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...
4.2K
Constitutive and Regulated Gene Expression01:27

Constitutive and Regulated Gene Expression

1.3K
Gene expression in prokaryotes is governed by constitutive and regulated systems, allowing cells to balance the production of essential proteins with adaptive responses to environmental changes.Constitutive Gene ExpressionConstitutive, or housekeeping, genes are continuously expressed as they encode proteins vital for fundamental cellular processes. These include enzymes for glycolysis, ribosomal components for protein synthesis, and proteins involved in DNA replication. Their constant...
1.3K
DNA Microarrays02:34

DNA Microarrays

21.3K
Microarrays are high-throughput and relatively inexpensive assays that can be automated to analyze large quantities of data at a time. They are used in genome-wide studies to compare gene or protein expression under two varied conditions, such as healthy and diseased states. Microarrays consist of glass or silica slides on which probe molecules are covalently attached through surface functionalization. Most commonly, the slides are prepared through the chemisorption of silanes to silica...
21.3K
Regulation of Expression Occurs at Multiple Steps02:24

Regulation of Expression Occurs at Multiple Steps

4.0K
4.0K
Regulation of Expression Occurs at Multiple Steps02:24

Regulation of Expression Occurs at Multiple Steps

26.6K
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...
26.6K

You might also read

Related Articles

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

Sort by
Same author

ScopeViewer: A Browser-Based Solution for Visualizing Large Biological Images.

GigaScience·2026
Same author

MicNet: integrating spatially resolved transcriptomes and pathology images by contrastive deep neural network.

Genome biology·2026
Same author

Computational identification of migrating T cells in spatial transcriptomics data.

JCI insight·2026
Same author

BiGER: Bayesian rank aggregation in genomics with extended ranking schemes.

Nature communications·2026
Same author

SpaFun: discovering domain-specific spatial expression patterns and new disease-relevant genes using functional principal component analysis.

Briefings in bioinformatics·2026
Same author

Advances in predicting omics profiles from imaging data.

Briefings in bioinformatics·2026
Same journal

PCSK5 promotes angiogenesis and cardiac repair after myocardial infarction.

Nature communications·2026
Same journal

PfApiAT2 is a proline transporter essential for the transmission of Plasmodium falciparum by the mosquito vector.

Nature communications·2026
Same journal

Transient distortions of the South Atlantic Anomaly radiation environments driven by electric fields.

Nature communications·2026
Same journal

Structural basis of the regulation by CDK11 kinase of early spliceosome activation and evidence for its proofreading by DHX15 helicase.

Nature communications·2026
Same journal

Structural and mechanistic insights into primer synthesis initiation by DNA primase.

Nature communications·2026
Same journal

Changes in heritability and shared environmentality of educational attainment across twentieth-century Norway.

Nature communications·2026
See all related articles

Related Experiment Video

Updated: Feb 16, 2026

Analyzing Multifactorial RNA-Seq Experiments with DiCoExpress
05:22

Analyzing Multifactorial RNA-Seq Experiments with DiCoExpress

Published on: July 29, 2022

4.0K

Genomic regression analysis of coordinated expression.

Ling Cai1,2, Qiwei Li2, Yi Du3

  • 1Children's Medical Center Research Institute at UT Southwestern Medical Center, 6000 Harry Hines Blvd, Dallas, TX, 75235, USA.

Nature Communications
|December 21, 2017
PubMed
Summary
This summary is machine-generated.

Genomic Regression Analysis of Coordinated Expression (GRACE) is a new method to analyze gene co-expression in cancer. It corrects for copy number alterations, revealing true biological regulation for better understanding of cancer gene networks.

More Related Videos

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

4.8K
An Allele-specific Gene Expression Assay to Test the Functional Basis of Genetic Associations
10:17

An Allele-specific Gene Expression Assay to Test the Functional Basis of Genetic Associations

Published on: November 3, 2010

23.4K

Related Experiment Videos

Last Updated: Feb 16, 2026

Analyzing Multifactorial RNA-Seq Experiments with DiCoExpress
05:22

Analyzing Multifactorial RNA-Seq Experiments with DiCoExpress

Published on: July 29, 2022

4.0K
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

4.8K
An Allele-specific Gene Expression Assay to Test the Functional Basis of Genetic Associations
10:17

An Allele-specific Gene Expression Assay to Test the Functional Basis of Genetic Associations

Published on: November 3, 2010

23.4K

Area of Science:

  • Genomics
  • Bioinformatics
  • Cancer Biology

Background:

  • Co-expression analysis is crucial for predicting gene function and identifying gene sets.
  • Somatic copy number alterations (SCNA) in cancer data create spurious co-expression patterns, masking true biological regulation.
  • Existing methods struggle to differentiate SCNA-driven co-expression from biologically relevant co-expression.

Purpose of the Study:

  • To develop a computational method that distinguishes true biological co-expression from SCNA-driven co-expression in cancer transcriptomic data.
  • To provide a robust tool for analyzing gene regulatory networks in cancer, independent of physical genomic proximity.
  • To enhance the understanding of transcriptional network rewiring in cancer.

Main Methods:

  • Introduced Genomic Regression Analysis of Coordinated Expression (GRACE), a novel method to adjust for SCNA effects in co-expression analysis.
  • Applied GRACE to analyze large-scale transcriptomic datasets, including The Cancer Genome Atlas (TCGA), Cancer Cell Line Encyclopedia (CCLE), and NCI60.
  • Developed a user-friendly web database for querying TCGA datasets analyzed with GRACE.

Main Results:

  • GRACE effectively adjusts for SCNA, revealing gene-gene co-expression patterns driven by biological regulation rather than genomic alterations.
  • Analysis of TCGA, CCLE, and NCI60 data demonstrated GRACE's ability to improve the identification of functional gene relationships.
  • The GRACE method provides a clearer view of how transcriptional networks are reorganized during cancer development.

Conclusions:

  • GRACE offers a significant advancement in analyzing gene co-expression within cancer genomics by mitigating the confounding effects of SCNA.
  • This method facilitates a more accurate understanding of gene regulatory mechanisms in cancer.
  • The publicly available GRACE database empowers researchers to explore cancer gene networks with enhanced accuracy.