Computational Metagenomics: State of the Art
Marco Antonio Pita-Galeana, Martín Ruhle, Lucía López-Vázquez, Guillermo de Anda‐Jáuregui, Enrique Hernández–Lemus
Abstract
Computational metagenomics has revolutionized our understanding of the human microbiome, enabling the characterization of microbial diversity, the prediction of functional capabilities, and the identification of associations with human health outcomes. This review provides a concise yet comprehensive overview of state-of-the-art computational approaches in metagenomics, alongside widely used methods and tools employed in amplicon-based metagenomics. It is intended as an introductory resource for new researchers, outlining key methodologies, challenges, and future directions in the field. We discuss recent advances in bioinformatics pipelines, machine learning (ML) models, and integrative frameworks that are transforming our understanding of the microbiome's role in health and disease. By addressing current limitations and proposing innovative solutions, this review aims to outline a roadmap for future research and clinical translation in computational metagenomics.