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Author Spotlight: Exploring Sex-Specific Glial Signatures and Therapeutic Leads for Alzheimer's Disease

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MetaFun: unveiling sex-based differences in multiple transcriptomic studies through comprehensive functional

Pablo Malmierca-Merlo¹, Rubén Sánchez-Garcia¹, Rubén Grillo-Risco¹

¹Computational Biomedicine Laboratory, Principe Felipe Research Center (CIPF), Eduardo Primo Yúfera Street, 3, 46012, Valencia, Spain.

Biology of Sex Differences

|August 27, 2024

Summary

MetaFun is a new web tool for analyzing transcriptomic data to find sex-based biological differences. It helps researchers understand sex as a variable in diseases for personalized medicine.

Keywords:

FAIR data Functional profiling Meta-analysis Personalized medicine RNA-sequencing Sex-based differences Systematic review Web tool

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Area of Science:

Genomics
Bioinformatics
Translational Research

Background:

Acknowledged sex-based differences in health require better analytical tools.
Current methods lack depth for analyzing sex as a variable in biomedical research.
A knowledge gap exists in sex-specific biological analysis.

Purpose of the Study:

Introduce MetaFun, a web-based tool for transcriptomic data meta-analysis.
Enable in-depth analysis of sex as a biological variable.
Enhance statistical power and biological soundness in sex-based research.

Main Methods:

MetaFun offers a comprehensive suite for transcriptomic data analysis.
Includes single-dataset analysis, differential gene expression, and functional enrichment.
Performs functional meta-analysis to identify consensus biological patterns across studies.

Main Results:

MetaFun identifies common patterns in biological processes, molecular functions, and cellular components altered between sexes.
Facilitates answering biological questions regarding sex-based alterations in transcriptomic studies.
Provides a platform for exploring sex differences in various diseases.

Conclusions:

Metafun is the first open-access web tool for identifying consensus biological functions across multiple transcriptomic datasets.
Aids in elucidating sex differences in numerous diseases.
Supports the generation of novel biological knowledge for personalized medicine, considering patient sex.