AUTHOR=Griffard-Smith Rachel , Schueddig Emily , Mahoney Diane E. , Chalise Prabhakar , Koestler Devin C. , Pei Dong 

TITLE=micRoclean: an R package for decontaminating low-biomass 16S-rRNA microbiome data

JOURNAL=Frontiers in Bioinformatics

VOLUME=Volume 5 - 2025

YEAR=2025

URL=https://www.frontiersin.org/journals/bioinformatics/articles/10.3389/fbinf.2025.1556361

DOI=10.3389/fbinf.2025.1556361

ISSN=2673-7647

ABSTRACT=In 16S-rRNA microbiome studies, cross-contamination and environmental contamination can obscure true biological signal. This contamination is particularly problematic in low-biomass studies, which are characterized by samples with a small amount of microbial DNA. Although multiple methods and packages for decontaminating microbiome data exist, there is no consensus on the most appropriate tool for decontamination based on the individual research study design and how to quantify the impact of removing identified contaminants to avoid over-filtering. To address these gaps, we introduce micRoclean, an open-source R package that contains two distinct microbiome decontamination pipelines with guidance on which to select based on the downstream goals of the research study and study design. This package integrates and expands on existing packages for microbiome decontamination and analysis for convenience of users. Furthermore, micRoclean also implements a filtering loss statistic to quantify the impact of decontamination on the overall covariance structure of the data. In this paper, we demonstrate the utility of micRoclean through implementation on example data, illustrating that micRoclean effectively and intuitively decontaminates microbiome data. Further, we demonstrate through a multi-batch simulated microbiome sample that micRoclean matches or outperforms tools with similar objectives. This package is freely available from GitHub repository rachelgriffard/micRoclean.