MBIOME: A comprehensive, reproducible, and open-source workflow for amplicon-based microbiome data analysis.

Authors

Gorostidi-Aicua, M., Otaegui-Chivite, A., Zabala, A., Moles, L., Otaegui, D.

Abstract

Microbiome analysis has become a pivotal tool in understanding the role of microbiota in human health and disease. However, the lack of standardized workflows, together with the limitations of proprietary software solutions, hampers reproducibility and flexibility. Here, we present Mbiome, an open-source, user-friendly and automated workflow designed to streamline amplicon-based microbiome analysis. Built upon QIIME2, Mbiome supports both bacterial (16S rRNA) and fungal (ITS) profiling, and is compatible with raw fastq files generated by Ion Torrent (IT) and Illumina (IL) sequencing platforms. The workflow guides users through an interactive setup process via a simple configuration file, enabling researchers with minimal bioinformatics experience to perform comprehensive analyses without writing code. Once configured, Mbiome automates major steps including quality control, taxonomic assignment, and {beta}-diversity analyses, functional predictions (via q2-metnet), and customizable visualizations and statistical analyses. Mbiome has been validated using real-world datasets from multiple sclerosis research projects, performing a comparison between different microbiome analysis approaches, including 16S hypervariable region reconstruction, amplicon-based strategies, and cross-platform sequencing (IT and IL), as well as against results obtained with Ion Reporter (IR) commercial software. This evaluation demonstrated its versatility and effectiveness across different sequencing platforms. Moreover, Mbiome provided improved flexibility, transparency, and taxonomic resolution compared to IR. By combining accessibility, reproducibility, and cross-platform compatibility, Mbiome lowers the barrier to microbiome data analysis and facilitates high-quality, standardized workflows in both research and applied settings. Mbiome is publicly available at https://github.com/MGorostidi/mbiome.

Preprint server: bioRxiv
The authors list and abstract were imported from bioRxiv on 01 Jul 2026.

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