ARUNA: Slice-based self-supervised imputation for upscaling DNA methylation sequencing assays

Abstract

Whole-genome bisulfite sequencing (WGBS) can provide near-comprehensive, base-resolution maps of DNA methylation, transforming our understanding of epigenetic regulation in development and disease, but its cost is often prohibitive for many studies. Reduced representation bisulfite sequencing (RRBS) offers a cost-effective alternative that profiles a CpG-enriched subset of the genome at base resolution. Similar sequencing protocols for both assays pose an opportunity for cross-assay integration, presenting an opportunity for massively increasing sample sizes at whole-genome resolution. However, existing imputation methods are designed for within-assay scenarios and cannot handle the substantial CpG coverage differences between WGBS and RRBS. We introduce ARUNA, a self-supervised denoising convolutional autoencoder that predicts genome-wide CpG-level methylation using only a small subset of observed methylation values and CpG coordinates. By modeling methylation "slices," spatially stacked windows that preserve local correlation structure, ARUNA captures biologically meaningful covariation while avoiding representation collapse. In simulation studies using the GTEx dataset, ARUNA successfully upscales RRBS-scale sparse methylomes (80-95% missingness) to whole-genome resolution, consistently outperforming baselines and maintaining robust performance across donor and tissue holdouts. When applied to real RRBS data from the ENCODE dataset, ARUNA outperformed state-of-the-art methods, with performance validated by matching upscaled RRBS samples to isogenic WGBS replicates. Source code for ARUNA can be found at https://github.com/ylaboratory/ARUNA.