yMGV: a database for visualization and data mining of published genome-wide yeast expression data.

Area of Science:

• Molecular Biology
• Bioinformatics
• Genomics

Background:

• Microarray technology generates large-scale gene expression data.
• Integrating diverse datasets is crucial for comprehensive analysis.
• Saccharomyces cerevisiae is a model organism for genetic studies.

Purpose of the Study:

• To develop an integrated online database for yeast gene expression profiles.
• To provide tools for analyzing and visualizing microarray data.
• To facilitate the discovery of gene classifications based on expression patterns.

Main Methods:

• Compilation of published genome-wide expression datasets for Saccharomyces cerevisiae.
• Development of a graphical, one-screen interface for data visualization.
• Implementation of tools for gene grouping based on similar transcription profiles.
• Inclusion of statistical tools for data assessment.

Main Results:

• The yeast Microarray Global Viewer (yMGV) database integrates 932 experiments from 39 publications.
• Contains over 6 million measurements of genome-wide expression comparisons.
• Provides a synthetic view of transcriptional expression profiles.
• Enables quick retrieval of experimental conditions affecting gene expression.

Conclusions:

• yMGV serves as an efficient tool for a comprehensive overview of microarray data.
• Facilitates the generation of new gene classifications.
• Supports critical assessment of published yeast gene expression data.