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.

16S rRNA gene amplicon sequencing is the most widely used approach for characterizing microbial communities, yet analyzing such data requires navigating a fragmented landscape of bioinformatics tools with distinct installation requirements, parameter settings, and data formats. Here we present 16S-Pipeline, an open-source, web-based platform that provides a complete workflow from raw FASTQ files to publication-ready statistical analyses. 16S-Pipeline automatically detects sequencing type (paired-end, single-end, long-read), variable region, and sequencing platform (Illumina, PacBio HiFi, Nanopore), then performs quality filtering, primer trimming, amplicon sequence variant (ASV) inference via DADA2, taxonomy assignment against SILVA v138.1, phylogenetic tree construction, and optional functional prediction via PICRUSt2. Downstream analyses include alpha and beta diversity, taxonomic composition visualization, differential abundance testing using five complementary methods (ALDEx2, DESeq2, ANCOM-BC2, LinDA, MaAsLin2) with consensus reporting, and KEGG pathway mapping. Built-in NCBI SRA integration enables downloading public datasets for re-analysis and generates submission metadata spreadsheets for data deposition. The interactive web interface built on FastAPI and Plotly Dash enables researchers to perform complex microbiome analyses without command-line expertise. 16S-Pipeline is freely available at https://github.com/tatsu1207/16S-Pipeline under the MIT License.