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 Experiment Videos

Ontologizing gene-expression microarray data: characterizing clusters with Gene Ontology.

Peter N Robinson1, Andreas Wollstein, Ulrike Böhme

  • 1Institute of Medical Genetics, Charité University Hospital, Humboldt University, Augustenburger Platz 1, 13353 Berlin, Germany. peter.robinson@charite.de

Bioinformatics (Oxford, England)
|February 7, 2004
PubMed
Summary
This summary is machine-generated.

Related Concept Videos

You might also read

Related Articles

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

Sort by
Same author

Apollo 3: Multi-Species Genome Curation.

bioRxiv : the preprint server for biology·2026
Same author

In-depth Human Phenotype Ontology Curation Boosts Prioritization Performance for Netherton Syndrome.

The British journal of dermatology·2026
Same author

Data-driven prioritization of mouse strains for improved preclinical modeling of rare and common disease.

bioRxiv : the preprint server for biology·2026
Same author

Diagnostic utility of clinical genome reanalysis in rare pediatric disorders using long-read sequencing.

HGG advances·2026
Same author

The promise of AlphaFold for gene structure annotation.

Nucleic acids research·2026
Same author

Familial Robertsonian Translocation, rob(14;21), with High Risk for Down Syndrome.

Cytogenetic and genome research·2026
Same journal

3DICE: Interpretable 3D Cross-Modal Learning for Drug-Target Interaction Prediction and Large-Scale Drug Discovery.

Bioinformatics (Oxford, England)·2026
Same journal

KASSPer: Kinase Active Site Structure Prediction using Protein and Ligand Language Models and Its Application to Virtual Screening.

Bioinformatics (Oxford, England)·2026
Same journal

IDR searcher: a search engine solution for public image resources.

Bioinformatics (Oxford, England)·2026
Same journal

KCFtools: Rapid alignment-free method for introgression screening and GWAS using k-mer profiles.

Bioinformatics (Oxford, England)·2026
Same journal

Meta2DB: Curated shotgun metagenomic feature sets and metadata for health state prediction.

Bioinformatics (Oxford, England)·2026
Same journal

conMItion: an R package adjusting confounding factors for associations in multi-omics.

Bioinformatics (Oxford, England)·2026
See all related articles

This study introduces a Java application for analyzing gene-expression microarray data. It offers a functional overview of Gene Ontology (GO) annotations within gene clusters, aiding biological interpretation.

Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • Gene-expression microarray data analysis is crucial for understanding cellular functions.
  • Interpreting large datasets often requires structured annotation and functional categorization.
  • Gene Ontology (GO) provides a standardized vocabulary for describing gene and protein functions.

Purpose of the Study:

  • To develop an XML-based Java application for visualizing gene-expression data analysis results.
  • To provide a function-oriented overview of cluster analysis outcomes using Gene Ontology terms.
  • To facilitate the interpretation of gene clusters by presenting GO annotations.

Main Methods:

  • Development of an XML-based Java application.
  • Implementation of cluster analysis for gene-expression microarray data.

Related Experiment Videos

  • Integration of Gene Ontology terms and associations for annotation.
  • Generation of HTML reports for data visualization.

    • Main Results:

    • The application generates an HTML page detailing the frequencies of explicit and implicit GO annotations per cluster.
    • Separate, linked HTML pages provide explicit GO annotations for individual genes within each cluster.
    • A function-oriented overview of cluster analysis results is achieved.

    Conclusions:

    • The developed application effectively summarizes and visualizes GO annotations from gene-expression data.
    • This tool aids researchers in interpreting the functional significance of gene clusters.
    • The approach enhances the discoverability of biological insights from microarray experiments.