The human microbiome is increasingly recognized as a key determinant of health and disease, yet methodological variability continues to limit reproducibility and clinical translation of findings. This review synthesizes current approaches in microbiome research, critically evaluating each step from sampling to sequencing and downstream bioinformatics. Pre-analytical factors such as sample type, collection method, preservation, and storage conditions profoundly affect microbial community profiles and remain a major source of bias. Nucleic acid extraction protocols and quality assessment strategies are discussed with emphasis on optimized lysis techniques, contamination controls, and DNA yield evaluation. Advances in sequencing technologies are highlighted, including 16S rRNA amplicon sequencing, shotgun metagenomics, third-generation long-read platforms, and emerging single-cell and minimal-input methods, each with specific advantages and limitations in taxonomic and functional resolution. Bioinformatics pipelines for taxonomic profiling, variant detection, phylogenetic inference, and functional annotation are compared, with attention to widely used reference databases such as RefSeq, GTDB, and SILVA. Integrative multi-omics approaches, including metatranscriptomics, metabolomics, and genome-scale metabolic modeling, are presented as powerful tools for linking microbial community structure to host physiology and disease mechanisms. Despite these advances, the lack of standardized workflows across pre-analytical, sequencing, and computational steps continues to hinder inter-study comparability and biomarker validation. This review aims to provide a methodological framework that highlights both strengths and limitations of current technologies while underlining the need for harmonized protocols to ensure reproducibility and accelerate the translation of microbiome research into clinical practice.
Ece et al. (Fri,) studied this question.