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The gene expression in cells is regulated at different stages: (i) transcription, (ii) RNA processing, (iii) RNA localization, and (iv) translation. Transcriptional regulation is mediated by regulatory proteins such as transcription factors, activators, or repressors—these control gene expression by initiating or inhibiting the transcription of genes. Once a precursor or pre-mRNA is produced, it undergoes post-transcriptional modification, including 5' capping, splicing, and the...
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Related Experiment Video

Updated: Jan 10, 2026

A Protocol for Using Gene Set Enrichment Analysis to Identify the Appropriate Animal Model for Translational Research
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Effect Size-Driven Pathway Meta-Analysis for Gene Expression Data.

Juan Antonio Villatoro-Garcia, Pablo Pedro Jurado-Bascon, Pedro Carmona-Saez

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    PubMed
    Summary
    This summary is machine-generated.

    This study introduces Gene Set Enrichment Meta-Analysis (GSEMA), a novel method for integrating omics data. GSEMA effectively addresses missing genes and platform differences, improving biological insights from complex datasets.

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    Area of Science:

    • Bioinformatics
    • Computational Biology
    • Genomics

    Background:

    • Omics datasets are proliferating, offering research opportunities but posing integration challenges due to missing genes and platform variations.
    • Traditional gene expression meta-analysis struggles with missing data, limiting biological interpretation.
    • Existing methods often focus on individual genes, leading to significant data loss.

    Purpose of the Study:

    • To develop a novel methodology, Gene Set Enrichment Meta-Analysis (GSEMA), for robust integration of omics datasets.
    • To overcome limitations of traditional meta-analysis methods in handling missing genes and platform discrepancies.
    • To enable pathway-level meta-analysis for enhanced biological insights.

    Main Methods:

    • GSEMA utilizes single-sample enrichment scoring to aggregate gene expression data into pathway-level matrices.
    • Meta-analysis techniques are applied to enrichment scores, preserving effect magnitude and directionality.
    • The method was validated using simulated data and case studies on Systemic Lupus Erythematosus and Parkinson's Disease.

    Main Results:

    • GSEMA effectively controls false positive rates compared to other methods.
    • The approach enables the definition of pathway activity across diverse datasets.
    • Meaningful biological interpretations were achieved in case studies.

    Conclusions:

    • GSEMA provides a robust framework for omics data integration and meta-analysis.
    • The methodology enhances biological interpretation by focusing on pathway-level activity.
    • GSEMA is implemented as an R package available on CRAN.