Mining data and metadata from the gene expression omnibus.

Area of Science:

• Bioinformatics
• Genomics
• Data Science

Background:

• Gene Expression Omnibus (GEO) hosts a rapidly growing number of publicly available gene expression datasets.
• Integrating these datasets offers significant potential for biological knowledge discovery.
• Current limitations in standardized metadata and uniform data processing impede large-scale dataset reuse.

Purpose of the Study:

• To review methodologies facilitating systematic curation and processing of GEO gene expression datasets.
• To identify trends in advanced metadata curation for public gene expression data.
• To summarize approaches for reprocessing the entire GEO repository data.

Main Methods:

• Systematic literature review of methodologies for gene expression dataset curation and processing.
• Analysis of trends in metadata standardization at study and sample levels.
• Summary of data reprocessing strategies applicable to large-scale repositories like GEO.

Main Results:

• Identified methodologies for systematic curation and processing of GEO datasets.
• Observed increasing trends towards advanced metadata curation practices.
• Summarized various approaches for uniform data reprocessing across the GEO repository.

Conclusions:

• Standardized metadata and uniform processing are crucial for enabling large-scale reuse of GEO datasets.
• Methodologies for systematic curation and reprocessing are essential for maximizing the value of public gene expression data.
• Continued development in these areas will facilitate integrated knowledge discovery from genomic repositories.