GTx-GUT: An Automated Pipeline for Analysis and Clinical Interpretation of Human Gut Microbiome Data
Gut microbiome; 16S rRNA sequencing; bioinformatics; automation; pipeline; artificial intelligence; QIIME 2; Snakemake
The gut microbiome plays a fundamental role in several physiological processes of the host and is associated with different pathological conditions, including obesity, type 2 diabetes, inflammatory bowel diseases, and neuropsychiatric disorders. Although increasingly relevant for characterizing microbial communities and investigating their impact on human health, microbiome sequencing data analysis involves multiple steps, tools, and parameters that hinder analytical standardization, compromise reproducibility, and make result interpretation more complex.
In this scenario, this work presents the development of GTX-GUT, an automated pipeline for gut microbiome analysis based on 16S rRNA gene sequencing data. Its central objective is to integrate, within a single, reproducible, and scalable workflow, the bioinformatics processing steps, the statistical contextualization of individual profiles against a reference population of healthy individuals, and the automated generation of interpretive reports with the support of artificial intelligence --- reducing the fragmentation typical of microbiome analyses and broadening access to this technology for laboratories and diagnostic centers without dedicated bioinformatics teams.
To evaluate the feasibility of the proposed solution, GTX-GUT was validated with a mock community sample of known microbial composition and applied to public samples from patients with different clinical conditions, spanning gastrointestinal, metabolic, neurological, and psychiatric disorders. The tools and architecture used to build the pipeline, as well as the experiments conducted, their results, and the conclusions of this work, are presented in detail in the corresponding chapters of this dissertation.
GTX-GUT thus represents a methodological contribution to bioinformatics applied to the gut microbiome, combining computational automation, analytical standardization, and support for scientific interpretation within a reproducible workflow.