Comprehensive Analysis of DNA Methylation Data with RnBeads (original) (raw)

RnBeads is an R package for comprehensive analysis of DNA methylation data obtained with any experimental protocol that provides single-CpG resolution. Supported assays include Infinium and EPIC microarrays and bisulfite sequencing protocols, and also MeDIP-seq and MBD-seq once the data have been preprocessed with DNA methylation level inference software. RnBeads implements an analysis workflow that is significantly more comprehensive than those of existing tools. It documents its results in a highly annotated and readable hypertext report, and it scales to the large sample sizes that are becoming the norm for DNA methylation analysis in human cohorts.

Various DNA methylation assays and input formats are supported. The pipeline implements state-of-the-art normalization techniques. Experimental quality control can be conducted and sample outliers and mix-ups can be identified. The package provides flexible methods for CpG and sample filtering. According to sample annotations, batch effects and phenotype covariates can be identified. DNA methylation distributions are analyzed and intergroup as well as intragroup variability in methylation profiles is quantified. Furthermore, differential methylation between groups of samples can be characterized. The analysis is based on individual CpGs as well as on predefined or custom genomic regions. Finally, methylation data can be exported in various formats including genome browser views. Comprehensive, highly interpretable reports containing method descriptions, publication grade plots and data tables are generated. Their HTML format facilitates easy tracking and comparison of analyses as well as exchanging results with collaboration partners. Due to its modularized concept, both first-time users and experts can conveniently perform analyses according to their individual demands. A single, comprehensive analysis run can be invoked by specifying only few parameters and executing a master command. Alternatively, a user may execute the steps of the pipeline individually.