Aug 2026· Frontiers in Microbiology· Vol 17· 0 citations· 188 references
Medicine
TL;DR
This review presents a practical, workflow-oriented guide to microbiome data analysis, from raw DNA sequence processing to statistical interpretation and biological insight, and highlights emerging technologies, including machine learning methods that are beginning to reshape the field.
Abstract
The rapid growth of microbiome research has been accompanied by an expanding but fragmented ecosystem of bioinformatic tools. Researchers now face a daunting array of software packages, pipelines, and web platforms spanning every stage of analysis, from quality control and taxonomic profiling to functional annotation and statistical interpretation. While this diversity offers flexibility, it also creates challenges in selecting appropriate tools and integrating them into coherent, reproducible workflows, particularly for researchers without formal computational training. This review presents a practical, workflow-oriented guide to microbiome data analysis, from raw DNA sequence processing to statistical interpretation and biological insight. We evaluate tools based on ease of use, methodological rigor, computational requirements, and community support, with particular attention to the trade-offs between command-line interface and web-based approaches. We cover both amplicon and shotgun metagenomic strategies for taxonomic and functional profiling, discuss reference database selection, and outline key statistical methods, including differential abundance testing and network inference. We also compare integrated platforms and web-based resources that lower barriers for non-computational researchers and discuss best practices for reproducibility and workflow design. Throughout, we highlight emerging technologies, including machine learning methods that are beginning to reshape the field. Overall, this review serves as a practical guide to navigating the microbiome bioinformatics landscape, helping bridge the gap between methodological complexity and the biological questions that drive microbiome research.
Application to a human sample from a patient with type 2 Diabetes Mellitus recovered a dysbiotic signature consistent with the literature, including reduced Firmicutes abundance, elevated Bacteroidetes and Proteobacteria, and a predominance of clinical associations within metabolic and gastrointestinal categories.
Rodrigo Lima Andrade, Tayná da Silva Fiúza, J. Kroll et al.· bioRxiv· 0 citations
This work optimized LC–MS/MS acquisition parameters for both DDA and DIA using a model microbiome, demonstrating how DIA enables increased sample throughput without compromising quantitative performance and establishing a scalable and cost-effective pipeline for metaproteomics of complex microbial communities.
Samantha Obermiller, Mary S. Lipton, P. Piehowski et al.· bioRxiv· 0 citations
Microbial communities play key roles in the transformation and cycling of elements ranging from required macronutrients to toxic metalloids. Next-generation sequencing has been applied across multiple ecosystems to probe the interplay of microbial community structure and functional potential with respect to elemental cycling. Shotgun metagenomics collects marker gene sequences without amplification and is costly for large numbers of samples and deep coverage. Conversely, amplicon sequencing of taxonomic marker genes, e.g. 16S and 18S rRNA, is cost-effective for large numbers of samples, but provides limited functional insight. A middle ground between the two approaches is needed to analyze community structure and functional potential within a sample while remaining cost-effective with high throughput. To address this need, we developed a standardized workflow for multiplexed amplicon sequencing from sample collection through data analysis for diverse sample types, including freshwater, sediments, and soils, that produces data and publication-ready figures for multiple taxonomic and functional genes for carbon, nitrogen, phosphorus, sulfur, and arsenic cycling for each sample analyzed. The workflow’s utility was shown by analyzing 11 taxonomic and functional gene amplicons sequenced from 25 samples with high technical replicate similarity. The workflow is named CAMASE for Compositional Analysis of Multiplex Amplicon Sequencing Experiments. This proof-of-concept shows that CAMASE economically produces standard amplicon sequencing outputs (ASV/OTU counts and taxonomy, PCA, and relative abundance plots) for hundreds of amplicon by sample combinations and provides specific recommendations for implementation. GRAPHICAL ABSTRACT Samples are collected in a preservative and material collected on filters prior to DNA extraction. Target gene amplicons are produced in parallel with internal barcodes enabling sequencing in a single run followed by compositional data analysis. All wet lab protocols, code markdowns, and templates for required metadata files are available at https://hansonlabgit.dbi.udel.edu/aprange/CAMASE. Created in BioRender. Bennett, A. (2026) https://BioRender.com/ymnojt0
Alexa J. Bennett, Ryan M. Moore, Craig W. Herbold et al.· bioRxiv· 0 citations
The large amount of high-throughput sequencing data generated in ecology, medicine, and pharmacology has increased the complexity of data analysis and interpretation. However, the microbiome and virome fields still lack a user-friendly and programming-free desktop application for comprehensive analysis of microbiome and virome data, with a particular gap in virome analysis and "dark matter" exploration. To address this gap, we introduce MicroWorldOmics, a plugin-based desktop application designed to offer a streamlined one-stop solution for life sciences and biomedical research. Its plugin-based architecture allows users to analyze data interactively and in parallel, simplifying tasks that typically require advanced bioinformatics skills. MicroWorldOmics is a comprehensive software suite tailored for microbiome and virome research, featuring 92 sub-applications across four main modules: epidemiology analysis, in-depth metagenomic/amplicon and virome profiling, and "dark matter" exploration. MicroWorldOmics leverages over 80 Python modules and 600 R packages for diverse bioinformatics, statistics, deep learning, and visualization tasks, accommodating multiple input and output formats including GFF3, FASTA, CSV, PNG, JPG, JSON, and TXT. To enhance user productivity, the software is compatible with Windows, Linux, and macOS systems, and includes demo data for easy benchmarking. In summary, MicroWorldOmics is intended to facilitate microbiome and virome data analysis for life sciences and biomedicine researchers without a programming background. It is available at https://hzaurzli.github.io/.
Runze Li, Wei Dong, Zhuang Yang et al.· Genomics, Proteomics & Bioin...· 0 citations
Amplicon-based techniques provide a rapid and cost-effective approach for profiling microbial communities. However, the observed microbial diversity is influenced by a wide range of factors, encompassing pre-analytical steps such as the choice of primers and target regions, as well as the bioinformatic pipeline, including the selection of tools, reference databases, and parameter settings. Several benchmarks are already available in the literature, but the updates to important tools and databases, namely LotuS3, the Ribosomal Database Project and GreenGenes2, prompted our investigation. In this study, we conducted a comprehensive benchmark of the main bioinformatic tools and databases. Using seven regions for three publicly available mock communities of increasing complexity, we tested 38 possible combinations of sequence resolution algorithms (DADA2 stand-alone, LotuS3 (DADA2/UPARSE)), taxonomic classifiers and search tools (Kraken2, DECIPHER, RDP, MMseqs2, Lambda, and Metaxa2), and databases (SILVA, GreenGenes2, RDP, RefSeq, and Metaxa2). The region V1-V3, coupled with DADA2+MMseqs2+SILVA, DADA2+Metaxa2, or LotuS3 (DADA2)+RDP yielded the highest-quality estimates of the true diversity according to the metrics. We also demonstrated that even certain dominant genera remain difficult to detect, and that the quantification of all genera can be substantially over- or under-estimated, even when using optimal combinations of tools and reference databases.
Louis-Maël Guéguen, Alban Mathieu, Olivier Périn et al.· bioRxiv· 0 citations
Applications across healthcare, environmental science, agriculture, biotechnology, and industry are reviewed with particular emphasis on clinical metagenomic next-generation sequencing (mNGS) for infectious disease diagnostics, antimicrobial resistance (AMR) surveillance, gut microbiome research, and precision medicine.
Ahmed Alsharksi· Razi Medical Journal· 0 citations
