RnBeads 2.0: comprehensive analysis of DNA methylation data
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SOFTWARE
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RnBeads 2.0: comprehensive analysis of DNA methylation data Fabian Müller1,7*†, Michael Scherer1,2*†, Yassen Assenov3*†, Pavlo Lutsik3*†, Jörn Walter4, Thomas Lengauer1 and Christoph Bock1,5,6*
Abstract DNA methylation is a widely investigated epigenetic mark with important roles in development and disease. Highthroughput assays enable genome-scale DNA methylation analysis in large numbers of samples. Here, we describe a new version of our RnBeads software - an R/Bioconductor package that implements start-to-finish analysis workflows for Infinium microarrays and various types of bisulfite sequencing. RnBeads 2.0 (https://rnbeads.org/) provides additional data types and analysis methods, new functionality for interpreting DNA methylation differences, improved usability with a novel graphical user interface, and better use of computational resources. We demonstrate RnBeads 2.0 in four re-runnable use cases focusing on cell differentiation and cancer. Keywords: DNA methylation, Computational epigenetics, Epigenome-wide association studies, Bisulfite sequencing, Epigenotyping microarrays, Integrative data analysis, Bioinformatics software tool, R/Bioconductor package
Background DNA methylation at CpG dinucleotides is a widely studied epigenetic mark that is involved in the regulation of cell state and relevant for a broad range of diseases. Changes in DNA methylation at promoters and enhancers have been associated with cell differentiation, developmental processes, cancer development, and regulation of the immune system. The vast majority of current assays for DNA methylation profiling use bisulfite treatment to selectively convert unmethylated cytosines (including 5-formyl-cytosine and 5-carboxy-cytosine) into uracil (which is subsequently replaced by thymine), while methylated cytosines (including 5-hydroxy-cytosine) remain unconverted. Bisulfite conversion thus transforms DNA methylation information into DNA sequence information that can be read by next-generation sequencing or DNA microarrays [1, 2]. Whole-genome bisulfite sequencing (WGBS) constitutes the current gold standard for DNA methylation profiling, given its genome-wide coverage and single-basepair resolution [3]. However, WGBS requires deep sequencing of the entire genome (which is a significant cost factor), while shallow * Correspondence: [email protected] † Fabian Müller, Michael Scherer, Yassen Assenov, and Pavlo Lutsik are co-first authors and contributed equally to this work. 1 Max Planck Institute for Informatics, Saarland Informatics Campus, 66123 Saarbrücken, Germany Full list of author information is available at the end of the article
sequencing leads to poor sensitivity for detecting small differences in DNA methylation. Reduced representation bisulfite sequencing (RRBS) offers a cost-effective alternative for profiling large sets of patient samples, by focusing the sequencing on a subset of the genome enriched using restriction enzymes [4]. RRBS is particularly useful for studying DNA methylation heterogeneity, which
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