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

What is Gene Expression?01:42

What is Gene Expression?

197.1K
Overview
Gene expression is the process in which DNA directs the synthesis of functional products, that is, proteins. Cells can regulate gene expression at various stages. It allows organisms to generate different cell types and enables cells to adapt to internal and external factors.
Genetic Information Flows from DNA to RNA to Protein
A gene is a stretch of DNA that serves as the blueprint for functional RNAs and proteins. Since DNA is made up of nucleotides and proteins consist of amino...
197.1K
What is Gene Expression?01:36

What is Gene Expression?

11.6K
A gene is a stretch of DNA that serves as the blueprint for functional RNAs and proteins. Since DNA is comprised  of nucleotides and proteins are comprised of amino acids, a mediator is required to convert the information encoded in DNA into proteins. This mediator is the messenger RNA (mRNA). mRNA copies the blueprint from DNA by a process called transcription. In eukaryotes, transcription occurs in the nucleus by complementary base-pairing with the DNA template. The mRNA is then...
11.6K
Cell Specific Gene Expression01:58

Cell Specific Gene Expression

16.6K
Multicellular organisms contain a variety of structurally and functionally distinct cell types, but the DNA in all the cells originated from the same parent cells. The differences in the cells can be attributed to the differential gene expression. Liver cells, whose functions include detoxification of blood, production of bile to metabolize fats, and synthesis of proteins essential for metabolism, must express a specific set of genes to perform their functions. Gene expression also varies with...
16.6K
Cell Specific Gene Expression01:58

Cell Specific Gene Expression

5.6K
5.6K
Chromatin Position Affects Gene Expression02:35

Chromatin Position Affects Gene Expression

24.9K
Chromatin is the massive complex of DNA and proteins packaged inside the nucleus. The complexity of chromatin folding and how it is packaged inside the nucleus greatly influences  access to genetic information. Generally, the nucleus' periphery is considered transcriptionally repressive, while the cell's interior is considered a transcriptionally active area. 
Topologically Associated Domains (TADs)
The 3-dimensional positioning of chromatin in the nucleus influences the...
24.9K
mRNA Stability and Gene Expression02:51

mRNA Stability and Gene Expression

6.7K
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
6.7K

You might also read

Related Articles

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

Sort by
Same author

Inhibition of DLX6 sensitizes lung adenocarcinoma to cisplatin via GPX4-dependent ferroptosis.

Cellular oncology (Dordrecht, Netherlands)·2026
Same author

RD-OMICS: An Integrative Multi-Omics Data Inventory in Rare Diseases.

bioRxiv : the preprint server for biology·2026
Same author

The urinary-metabolite-based lung cancer index (uLCI): an interpretable machine-learning risk model for early-stage disease.

medRxiv : the preprint server for health sciences·2026
Same author

Tissue-aware elastic net decomposition reveals shared and lineage-specific drug response biomarkers.

bioRxiv : the preprint server for biology·2026
Same author

Context-dependent correlations mislead transcriptomic network inference in bulk and single-cell data.

bioRxiv : the preprint server for biology·2026
Same author

Resources and applications of public biomedical data.

Frontiers in bioinformatics·2026

Related Experiment Video

Updated: Feb 13, 2026

Author Spotlight: Emerging Technologies and Advanced Tools for Decoding Metabolomics Data Analysis
07:11

Author Spotlight: Emerging Technologies and Advanced Tools for Decoding Metabolomics Data Analysis

Published on: November 10, 2023

3.3K

IntLIM: integration using linear models of metabolomics and gene expression data.

Jalal K Siddiqui1, Elizabeth Baskin1, Mingrui Liu1

  • 1Department of Biomedical Informatics, College of Medicine, The Ohio State University, Columbus, OH, USA.

BMC Bioinformatics
|March 7, 2018
PubMed
Summary

This study introduces IntLIM, an R package for integrating transcriptomic and metabolomic data to identify gene-metabolite associations specific to disease phenotypes. IntLIM aids in interpreting metabolomic data and discovering novel biomarkers and gene targets.

Keywords:
IntegrationLinear ModelingMetabolomicsTranscriptomics

More Related Videos

Using an Automated Cell Counter to Simplify Gene Expression Studies: siRNA Knockdown of IL-4 Dependent Gene Expression in Namalwa Cells
10:34

Using an Automated Cell Counter to Simplify Gene Expression Studies: siRNA Knockdown of IL-4 Dependent Gene Expression in Namalwa Cells

Published on: April 14, 2010

16.0K
Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches
09:47

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches

Published on: December 15, 2023

1.9K

Related Experiment Videos

Last Updated: Feb 13, 2026

Author Spotlight: Emerging Technologies and Advanced Tools for Decoding Metabolomics Data Analysis
07:11

Author Spotlight: Emerging Technologies and Advanced Tools for Decoding Metabolomics Data Analysis

Published on: November 10, 2023

3.3K
Using an Automated Cell Counter to Simplify Gene Expression Studies: siRNA Knockdown of IL-4 Dependent Gene Expression in Namalwa Cells
10:34

Using an Automated Cell Counter to Simplify Gene Expression Studies: siRNA Knockdown of IL-4 Dependent Gene Expression in Namalwa Cells

Published on: April 14, 2010

16.0K
Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches
09:47

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches

Published on: December 15, 2023

1.9K

Area of Science:

  • Genomics
  • Metabolomics
  • Systems Biology
  • Bioinformatics

Background:

  • Integrating transcriptomic and metabolomic data is crucial for understanding disease mechanisms and identifying biomarkers.
  • Large cohort studies increasingly generate multi-omics data, necessitating user-friendly tools for data integration.
  • Traditional pathway/network approaches may be limited with snapshot data and unidentified metabolites.

Purpose of the Study:

  • To develop a simple linear modeling approach for identifying phenotype-specific gene-metabolite associations.
  • To create an accessible, open-source tool for researchers without computational expertise.
  • To facilitate the discovery of novel gene-metabolite relationships and potential therapeutic targets.

Main Methods:

  • A linear model (metabolite ~ gene + phenotype + gene:phenotype) was employed to assess gene-metabolite relationships across different phenotypes.
  • Statistical interaction p-values were computed for all gene-metabolite pairs to identify significant associations.
  • Significant gene-metabolite pairs were clustered based on the directionality of their associations.

Main Results:

  • The developed R package, IntLIM, includes a user-friendly R Shiny web interface for integrative analysis.
  • IntLIM successfully identified known tumor-specific gene-metabolite associations in cancer cell lines and human breast tumor tissues.
  • The tool revealed novel, biologically relevant gene-metabolite relationships, particularly in pathways like glutamine metabolism.

Conclusions:

  • IntLIM offers a user-friendly and reproducible framework for integrating transcriptomic and metabolomic data.
  • The package facilitates the interpretation of metabolomic data and the discovery of novel gene-metabolite associations.
  • IntLIM is publicly available on GitHub with comprehensive documentation and sample data.