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SimpleMicrobiome: An integrated web-based platform for streamlined microbiome data analysis and visualization
Seong-In Na, Juhee Kim, So-Yeon Kim, Jin Park, Yong-Joon Cho
Received June 11, 2026  Accepted August 4, 2026  Published online September 18, 2026  
DOI: https://doi.org/10.71150/jm.2606011    [Epub ahead of print]
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AbstractAbstract PDFSupplementary Material

Microbiome studies require multiple analytical steps after initial sequence processing. These steps commonly include data harmonization, preprocessing, taxonomic profiling, diversity analysis, differential abundance testing, predictive modeling, network inference, and preparation of publication-ready outputs. Although robust packages are available for many of these tasks, routine use often depends on command-line workflows, repeated data reformatting, and method-specific scripting. These requirements can limit accessibility for experimental researchers and complicate consistent analysis across interdisciplinary teams. We developed SimpleMicrobiome, a web-based R Shiny platform that integrates established microbiome analysis methods into a single interactive downstream workflow. The application accepts standard abundance, taxonomy, and metadata tables, supports interactive preprocessing and sample filtering, and provides modules for taxa profile visualization, alpha and beta diversity analysis, ANCOM-BC2 and MaAsLin2 differential abundance testing, Random Forest modeling with SHAP-based interpretation, microbial association network inference using SparCC and SPIEC-EASI through NetCoMi, correlation heatmaps, and dbRDA/CAP-style association biplots. The platform is implemented as a modular Shiny application so that preprocessing choices are propagated across downstream analyses, results can be exported as figures and tables, and the same application can be run through the public server, source-code installation, or a Docker image.

SimpleMicrobiome consolidates major downstream microbiome analysis tasks in an accessible browser-based environment while retaining links to established analytical frameworks. The platform may reduce technical barriers for non-programming users, improve consistency across exploratory and reporting-oriented analyses, and support collaborative microbiome research. The public application is available at https://simplemicrobiome.mglab.org, the source code is available at https://github.com/yjcho2252/SimpleMicrobiome, and a Docker image for local deployment is available at https://hub.docker.com/r/mglab2252/simplemicrobiome.

Development of Molecular Biological Methods to Analyze Bacterial Species Diversity in Freshwater and Soil Ecosystems
Dong-Hun Lee , Sung-Ae Noh , Chi-Kyung Kim
J. Microbiol. 2000;38(1):11-17.
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A new method was developed for the rapid analysis of diverse bacterial species in the natural envi-ronment. Our method is based on PCR-single-strands-conformation polymorphism (PCR-SSCP) and selective isolation technique of single-stranded DNA. Variable V3 fragments of 16S rDNA were amplified by PCR with bacterial 16S rDNA primers, where one of the primers was biotinylated at the 5'-end. The biotinylated strands of the PCR products were selectively isolated by using streptavidin paramagnetic particles and a magnetic stand, to prevent SSCP analysis producing heteroduplexes from heterogeneous DNA samples. The selected strands were separated by electrophoresis on a polyacrylamide gel, and detected by silver staining. Analysis of PCR products from 8 bacterial strains demonstrated their characteristic DNA band patterns. In addition, changes in the structure of the bacterial community and species diversity in the microcosm treated with phenol could be monitored. After 3 weeks of incubation, phenol and its intermediate, 2-hydroxy-muconic-semialdehyde, were degraded by indig-enous bacteria. These dominating bacterial populations were identified as strong bands on an SSCP gel. Therefore, this study provides useful tools for microbial community analysis of natural habitats.

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