amplysis: an R package for microbial composition and diversity analysis using 16S rRNA amplicon data
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Updated: Jan 10, 2026

Microbiota Analysis Using Two-step PCR and Next-generation 16S rRNA Gene Sequencing
Published on: October 15, 2019
Ziming Su1,2, Xinyu Zhang2,3, Qiming Wang4
1School of Resources, Environment and Materials, Guangxi University, Daxue Road 100, Nanning 530004, Guangxi, China.
Abstract:
The downstream analysis of 16S rRNA sequencing data remains a significant challenge for researchers lacking extensive bioinformatics expertise, often requiring proficiency in diverse tools and methodologies. To address this, we present amplysis, an R package designed to streamline the analysis and visualization of 16S rRNA amplicon sequencing data through an intuitive, code-light workflow. amplysis integrates data importing, processing, statistical analysis, and visualization into a unified framework. Key features include data normalization, microbial composition profiling, alpha/beta diversity analysis, ordination methods (e.g. Principal Component Analysis), and publication-ready visualization tools. The package's utility was demonstrated through three case studies, one of which analyzed microbial community responses to hexachlorocyclohexane (HCH) degradation in groundwater environments. Using amplysis, we efficiently generated phylum/genus-level abundance plots, alpha-diversity indices, and Principal Coordinates Analysis ordination, revealing significant shifts in community structure and diversity under HCH stress. The other case studies utilized publicly available data from published studies by other researchers. These results underscore the package's ability to simplify complex analyses while ensuring reproducibility and high-quality output. By integrating modular, user-friendly functions, amplysis lowers the barrier to robust microbiome data exploration. The package is available on GitHub (https://github.com/min-perilla/amplysis), offering a valuable resource for researchers in microbial ecology and environmental genomics.
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