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Related Concept Videos

Genome-wide Association Studies-GWAS01:11

Genome-wide Association Studies-GWAS

Genome-wide association studies or GWAS are used to identify whether common SNPs are associated with certain diseases. Suppose specific SNPs are more frequently observed in individuals with a particular disease than those without the disease. In that case, those SNPs are said to be associated with the disease. Chi-square analysis is performed to check the probability of the allele likely to be associated with the disease.
GWAS does not require the identification of the target gene involved in...
DNA Microarrays02:34

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...

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Related Experiment Video

Updated: Jun 18, 2026

Integration of Bioinformatics Approaches and Experimental Validations to Understand the Role of Notch Signaling in Ovarian Cancer
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Genevestigator v3: a reference expression database for the meta-analysis of transcriptomes.

Tomas Hruz1, Oliver Laule, Gabor Szabo

  • 1Institute of Theoretical Computer Science, ETH Zurich, 8092 Zurich, Switzerland.

Advances in Bioinformatics
|December 4, 2009
PubMed
Summary

Genevestigator V3 enhances gene expression analysis for biologists with improved querying and new tools for identifying biomarkers and modeling networks. This web-based system facilitates discovery research and hypothesis validation.

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Molecular Biology

Background:

  • Previous versions of Genevestigator had limitations in scalability, multi-organism data handling, and computationally intensive analyses.
  • Gene expression data analysis is crucial for understanding biological processes and validating research hypotheses.

Purpose of the Study:

  • To introduce Genevestigator V3, a novel meta-analysis system designed to overcome the limitations of earlier versions.
  • To provide biologists with enhanced tools for exploring gene expression across diverse biological contexts.

Main Methods:

  • Development of a new client/server architecture for improved scalability and performance.
  • Large-scale manual curation and quality control of microarray data for multiple organisms.
  • Curation of pathway data for *mouse* and *Arabidopsis*.

Main Results:

  • Genevestigator V3 offers improved querying capabilities for gene expression data.
  • New tools enable analysis of gene expression in various contexts, biomarker identification, gene clustering into expression modules, and modeling of expression responses within metabolic and regulatory networks.
  • The system functions as a reference expression database with user-friendly tools.

Conclusions:

  • Genevestigator V3 represents a significant advancement in web-based gene expression analysis tools.
  • The enhanced system supports discovery research and hypothesis validation by providing comprehensive data and analytical functionalities.
  • The integration of curated pathway data further enriches the system's utility for biological research.