TITLE:
Microbiome Analysis: Tools and Techniques for Understanding Human Gut Microbiota
AUTHORS:
Sabeen Raza, Mirjana Menkovska
KEYWORDS:
Gut Microbiota, Meta-Omics Technologies, Bioinformatics Pipelines, Precision Nutrition, Compositional Bias, Multi-Omics Integration, Metagenomics, Metabolomics, Spatially Resolved Microbiomics, Pharmacomicrobiomics, Artificial Intelligence in Microbiome
JOURNAL NAME:
Advances in Bioscience and Biotechnology,
Vol.17 No.6,
June
15,
2026
ABSTRACT: A comprehensive understanding of how the human gut microbiome impacts systemic health and metabolic regulation necessitates advancing from traditional marker gene sequencing to sophisticated, data-driven systems biology approaches. The interpretation of high-dimensional datasets in nutritional microbiome research is often impeded by substantial genetic variability, statistical noise, and inherent compositional bias. This review provides an analysis of the current meta-omics technologies—covering metagenomics, metatranscriptomics, metaproteomics, and metabolomics—and their corresponding bioinformatic pipelines that convert raw biological samples into clinically meaningful nutritional information. We assess established analytical frameworks such as QIIME 2, bioBakery 3, and MaAsLin 3, together with multi-omics integration tools including DIABLO and MOFA+, which address compositional data complexities. Furthermore, this manuscript discusses innovative methodologies transforming the discipline, including single-cell metagenomics (SiC-seq), telomere-to-telomere (T2T) long-read sequencing, spatially resolved microbiomics, and the use of artificial intelligence through foundational large language models (LLMs). Through clinical case studies involving pediatric irritable bowel syndrome, epilepsy, and pharmacomicrobiomics (with a focus on GLP-1 agonists), the practical value of data-driven dietary intervention is demonstrated. Ongoing enhancement of analytical techniques and adherence to standardized reporting practices remain fundamental for resolving reproducibility challenges and progressing towards the clinical implementation of precision nutrition and the development of in silico digital twins for individualized healthcare.