RnBeads
RnBeads is an open-source R package for the comprehensive analysis of DNA methylation data at single-CpG resolution. It supports Infinium and EPIC microarrays, bisulfite sequencing protocols, and preprocessed MeDIP-seq and MBD-seq data. RnBeads combines quality control, normalization, filtering, exploratory analysis, covariate assessment, and differential methylation analysis in a modular workflow that scales to large cohort studies. Results are documented in highly annotated HTML reports containing method descriptions, publication-ready plots, and detailed data tables.
Key benefits
Supports multiple DNA methylation assays and input formats
Implements state-of-the-art normalization and flexible CpG and sample filtering
Identifies sample outliers, potential sample mix-ups, batch effects, and phenotype-associated covariates
Analyzes methylation distributions and within- and between-group variability
Performs differential methylation analysis at individual CpGs and predefined or custom genomic regions
Generates comprehensive, shareable HTML reports with publication-ready visualizations
Scales to large sample numbers and can be run through a master command, individual pipeline modules, or a graphical user interface
Applications
Quality control and preprocessing of DNA methylation datasets
Analysis of Infinium, EPIC, and supported mouse methylation arrays
Analysis of whole-genome and reduced-representation bisulfite sequencing data
Detection of batch effects, phenotype covariates, outliers, and sample mix-ups
Identification and characterization of differentially methylated CpGs and regions
Comparison of methylation variability within and between sample groups
Export of methylation data in multiple formats, including genome-browser-compatible views
Intended use
RnBeads is intended for epigenetics researchers, molecular biologists, bioinformaticians, and core facilities working with DNA methylation data. Its automated workflow and graphical interface make it accessible to first-time users, while its modular design and extensive configuration options support advanced and customized analyses in R.
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