Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Evolutionary Relationships through Genome Comparisons

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...

DNA Microarrays

DNA Microarrays

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...

What is Gene Expression?

What is Gene Expression?

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...

Cell Specific Gene Expression

Cell Specific Gene Expression

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...

Cell Specific Gene Expression

Cell Specific Gene Expression

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...

You might also read

Related Articles

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

Sort by

Same author

SenSet defines cell-type specific senescence signatures in the aged human lung.

The EMBO journal·2026

Same author

Utility of Fine-Needle Aspiration Cytology Smears for Molecular Analysis of Lung Adenocarcinomas: A Comparative Analysis of Next-Generation Sequencing Quality Metrics.

Diagnostic cytopathology·2026

Same author

MolQuery: Prediction of Lipid Synthesizability Using Active Learning.

ACS omega·2026

Same author

Deep Batch Active Learning for Protein Structure Modeling.

Journal of computational biology : a journal of computational molecular cell biology·2026

Same author

Evaluation of statistical differential analysis methods for identification of senescent cells using single-cell transcriptomics.

Cell reports methods·2026

Same author

Single-cell atlas of human lung aging identifies cell type dyssynchrony and increased transcriptional entropy.

Nature communications·2026

Search research articles

Related Experiment Video

Updated: May 21, 2026

A Protocol for Using Gene Set Enrichment Analysis to Identify the Appropriate Animal Model for Translational Research

A Protocol for Using Gene Set Enrichment Analysis to Identify the Appropriate Animal Model for Translational Research

Published on: August 16, 2017

Matching experiments across species using expression values and textual information.

Aaron Wise¹, Zoltán N Oltvai, Ziv Bar-Joseph

¹Lane Center for Computational Biology, Carnegie Mellon University Pittsburgh, PA 15213, USA.

Bioinformatics (Oxford, England)

|June 13, 2012

Summary

This summary is machine-generated.

We developed a novel co-training method to automatically match gene expression datasets across species. This approach improves cross-species comparisons, aiding in the discovery of disease-related genes and biological pathways.

More Related Videos

Analyzing Multifactorial RNA-Seq Experiments with DiCoExpress

Analyzing Multifactorial RNA-Seq Experiments with DiCoExpress

Published on: July 29, 2022

A Bioinformatics Pipeline for Investigating Molecular Evolution and Gene Expression using RNA-seq

A Bioinformatics Pipeline for Investigating Molecular Evolution and Gene Expression using RNA-seq

Published on: May 28, 2021

Related Experiment Videos

Last Updated: May 21, 2026

A Protocol for Using Gene Set Enrichment Analysis to Identify the Appropriate Animal Model for Translational Research

A Protocol for Using Gene Set Enrichment Analysis to Identify the Appropriate Animal Model for Translational Research

Published on: August 16, 2017

Analyzing Multifactorial RNA-Seq Experiments with DiCoExpress

Analyzing Multifactorial RNA-Seq Experiments with DiCoExpress

Published on: July 29, 2022

A Bioinformatics Pipeline for Investigating Molecular Evolution and Gene Expression using RNA-seq

A Bioinformatics Pipeline for Investigating Molecular Evolution and Gene Expression using RNA-seq

Published on: May 28, 2021

Area of Science:

Bioinformatics
Computational Biology
Genomics

Background:

Increasing volume of public gene expression data necessitates advanced analytical techniques.
Cross-species expression comparison is crucial for evolutionary and developmental studies, and disease gene identification.
Matching experiments across species is a key challenge due to the dynamic nature of expression data.

Purpose of the Study:

To develop and validate a method for automatically identifying similar gene expression datasets across different species.
To enhance the utility of public gene expression data for comparative biological research.

Main Methods:

A co-training algorithm was employed, integrating expression similarity models with text-based analysis of experimental metadata.
The method combines models of expression similarity and accompanying text to identify cross-species experimental matches.
Performance was evaluated against methods relying solely on expression similarity.

Main Results:

The co-training method significantly outperforms previous approaches based on expression similarity alone.
Expert validation confirmed that the identified cross-species matches reflect genuine biological similarities.
Application to human and mouse data successfully identified known and novel cycling genes and potential diabetes-related genes.

Conclusions:

The developed method effectively identifies homologous gene expression patterns across species.
This facilitates the discovery of novel candidate genes in model organisms for studying human diseases.
The approach enhances the potential for utilizing large-scale gene expression datasets in comparative genomics and disease research.