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

Genome Annotation and Assembly03:36

Genome Annotation and Assembly

The genome refers to all of the genetic material in an organism. It can range from a few million base pairs in microbial cells to several billion base pairs in many eukaryotic organisms. Genome assembly refers to the process of taking the DNA sequencing data and putting it all back together in a correct order to create a close representation of the original genome. This is followed by the identification of functional elements on the newly assembled genome, a process called genome annotation.
Correlation of Experimental Data01:23

Correlation of Experimental Data

Dimensional analysis simplifies complex physical problems and guides experimental investigations, but it does not provide complete solutions. It identifies the dimensionless groups that influence a phenomenon, but experimental data is needed to establish the specific relationships and validate theoretical predictions.
For example, a spherical particle moving through a viscous fluid experiences drag. Dimensional analysis shows that the drag force depends on the particle's diameter, velocity, and...
Protein Networks02:26

Protein Networks

An organism can have thousands of different proteins, and these proteins must cooperate to ensure the health of an organism. Proteins bind to other proteins and form complexes to carry out their functions. Many proteins interact with multiple other proteins creating a complex network of protein interactions.
These interactions can be represented through maps depicting protein-protein interaction networks, represented as nodes and edges. Nodes are circles that are representative of a protein,...
Correlation and Causation01:27

Correlation and Causation

Correlation and CausationStatistical tests can calculate whether there is a relationship, or correlation, between independent and dependent variables. A relationship between variables shows correlation, but it does not show cause-and-effect. A direct cause-and-effect relationship requires additional controlled experiments. If no consistent relationship exists between the variables, then there is no correlation.Correlation versus CausationIf the dependent variable increases or decreases when the...
Complementary DNA01:44

Complementary DNA

Overview
Evolutionary Relationships through Genome Comparisons02:54

Evolutionary Relationships through Genome Comparisons

Genome comparison is one of the excellent ways to interpret the evolutionary relationships between organisms. The basic principle of genome comparison is that if two species share a common feature, it is likely encoded by the DNA sequence conserved between both species. The advent of genome sequencing technologies in the late 20th century enabled scientists to understand the concept of conservation of domains between species and helped them to deduce evolutionary relationships across diverse...

You might also read

Related Articles

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

Sort by
Same author

Translational insights into extracellular vesicles in peritoneal dialysis.

Peritoneal dialysis international : journal of the International Society for Peritoneal Dialysis·2026
Same author

Integrating multi-omics data for next-generation cancer research and precision medicine.

Clinical & translational oncology : official publication of the Federation of Spanish Oncology Societies and of the National Cancer Institute of Mexico·2026
Same author

An Integrative Framework Identifies Cooperative Targeting of Host Pathways by Tick Salivary miRNAs.

Computational and structural biotechnology journal·2026
Same author

Proteomics of human duodenum in pre-diabetes and type 2 diabetes reveals potential novel therapeutic targets for aetiology and therapeutics.

Clinical proteomics·2026
Same author

Metabolic reprogramming regulates histone lactylation during zebrafish caudal fin regeneration.

iScience·2026
Same author

Integrative Proteomics of Extracellular Vesicles from hiPSC-Derived Cardiac Organoids Reveals Heart Tissue-like Molecular Representativity.

International journal of molecular sciences·2026
Same journal

Mapping the 3D Chromosome Organization of a Biosynthetic Gene Cluster by Capture Hi-C (CHi-C).

Methods in molecular biology (Clifton, N.J.)·2026
Same journal

Mapping the 3D Chromosome Organization of Streptomyces by Hi-C.

Methods in molecular biology (Clifton, N.J.)·2026
Same journal

CUT&Tag Epigenomic Profiling of Biosynthetic Gene Clusters in Arabidopsis thaliana.

Methods in molecular biology (Clifton, N.J.)·2026
Same journal

Rhizobium rhizogenes-Mediated Hairy Root Transformation Protocol for Lotus japonicus and Other Legumes.

Methods in molecular biology (Clifton, N.J.)·2026
Same journal

Characterization of Bioactive Saponins from Sea Cucumbers.

Methods in molecular biology (Clifton, N.J.)·2026
Same journal

Methods for Functional Validation of Terpenoid Metabolic Clusters in Nicotiana benthamiana and Aspergillus oryzae.

Methods in molecular biology (Clifton, N.J.)·2026
See all related articles

Related Experiment Video

Updated: Jun 18, 2026

CorrelationCalculator and Filigree: Tools for Data-Driven Network Analysis of Metabolomics Data
07:11

CorrelationCalculator and Filigree: Tools for Data-Driven Network Analysis of Metabolomics Data

Published on: November 10, 2023

Algorithms and methods for correlating experimental results with annotation databases.

Michael Hackenberg1, Rune Matthiesen

  • 1Bioinformatics, Parque Technológico de Bizkaia, Derio, Bizkaia, Spain.

Methods in Molecular Biology (Clifton, N.J.)
|December 4, 2009
PubMed
Summary
This summary is machine-generated.

Identifying differentially expressed genes in disease is crucial for understanding pathology. Ontological analysis of these gene lists helps uncover key biomarkers and biological insights using established databases and tools.

More Related Videos

Annotation of Plant Gene Function via Combined Genomics, Metabolomics and Informatics
08:09

Annotation of Plant Gene Function via Combined Genomics, Metabolomics and Informatics

Published on: June 17, 2012

Applying Cheminformatics to Develop a Structure Searchable Database of Analytical Methods
05:34

Applying Cheminformatics to Develop a Structure Searchable Database of Analytical Methods

Published on: June 6, 2025

Related Experiment Videos

Last Updated: Jun 18, 2026

CorrelationCalculator and Filigree: Tools for Data-Driven Network Analysis of Metabolomics Data
07:11

CorrelationCalculator and Filigree: Tools for Data-Driven Network Analysis of Metabolomics Data

Published on: November 10, 2023

Annotation of Plant Gene Function via Combined Genomics, Metabolomics and Informatics
08:09

Annotation of Plant Gene Function via Combined Genomics, Metabolomics and Informatics

Published on: June 17, 2012

Applying Cheminformatics to Develop a Structure Searchable Database of Analytical Methods
05:34

Applying Cheminformatics to Develop a Structure Searchable Database of Analytical Methods

Published on: June 6, 2025

Area of Science:

  • Biomedical Research
  • Molecular Biology
  • Bioinformatics

Background:

  • Differential gene expression analysis is vital for understanding disease mechanisms.
  • Identifying key biomarkers is essential for characterizing pathologies at a molecular level.
  • Linking gene data to biological knowledge bases is necessary for comprehensive understanding.

Purpose of the Study:

  • To survey methods for ontological analysis of gene lists.
  • To introduce important tools for ontological analysis in biomedical research.
  • To explain how ontological analysis provides insights into biological systems.

Main Methods:

  • Gene lists from differential expression studies are analyzed.
  • Ontological analysis identifies enriched and depleted gene properties and functions.
  • Utilizes biological knowledge stored in databases.

Main Results:

  • Ontological analysis reveals significant biological insights.
  • Enriched and depleted functions highlight key molecular pathways.
  • Identified genes serve as potential biomarkers for pathologies.

Conclusions:

  • Ontological analysis is a standard and powerful procedure for interpreting large gene lists.
  • This approach facilitates a deeper understanding of biological systems under pathologic conditions.
  • The chapter provides a foundational overview of methods and tools for this analysis.