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Microbiota Analysis Using Two-step PCR and Next-generation 16S rRNA Gene Sequencing

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Published on: October 15, 2019

Multi-omic integration of microbiome data for identifying disease-associated modules.

Efrat Muller, Itamar Shiryan, Elhanan Borenstein

Biorxiv : the Preprint Server for Biology

|July 18, 2023

Summary

MintTea integrates multi-omic microbiome data to identify disease-associated modules. This method reveals coherent mechanisms underlying microbiome-related diseases, offering systems-level insights for metabolic syndrome and colorectal cancer.

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Area of Science:

Microbiome Research
Systems Biology
Bioinformatics

Background:

The human gut microbiome plays a critical role in health and disease.
Multi-omic studies are crucial for understanding microbiome-disease interactions.
Current analytical methods struggle to integrate multi-omic data effectively, limiting mechanistic insights.

Conclusions:

MintTea offers a robust framework for integrating multi-omic microbiome data to uncover disease mechanisms.
The method facilitates the generation of integrated, multi-omic hypotheses for microbiome-related diseases.
MintTea provides systems-level insights into coherent mechanisms governing microbiome-disease interactions.