RICO enables integrated analysis of ribosomal DNA copy number and methylation from nanopore long-read sequencing

Ribosomal RNA (rRNA) provides the structural and catalytic core of ribosomes and is encoded by ribosomal RNA genes (rDNA) arranged in tandem repeat arrays. rDNA copy number (CN) is highly dynamic, representing a clinically relevant form of structural variation, but its accurate quantification has been challenging due to its highly repetitive and GC-rich nature. Here, we present RICO (RIbosomal dna Integrated COpy number and methylation analysis), a novel computational pipeline for integrated estimation of rDNA CN and methylation using Oxford Nanopore long-read sequencing. RICO leverages long sequencing reads that span entire rDNA repeats, mapped to an rDNA-augmented reference genome, and normalizes coverage using a single-copy gene panel. We demonstrate that RICO provides accurate rDNA CN estimates in simulated datasets and reproducible measurements across human samples, with strong agreement to short-read sequencing and PCR-based methods. Furthermore, RICO reproduces a ~40% reduction in rDNA CN in Atrx-knockout mouse embryonic stem cells, consistent with established effects of ATRX loss on rDNA CN. RICO also captures an increased total and active rDNA CN in malignant cells from a MYC-driven B-cell lymphoma mouse model, in agreement with prior studies. Applying RICO to independent human cohorts, we uncover that individuals with higher total rDNA CN consistently exhibit higher fractions of highly methylated rDNA copies, suggesting a dosage compensation mechanism that potentially maintains a similar number of active rDNA copies across individuals. Together, RICO enables integrated analysis of rDNA CN and methylation state, providing a scalable framework for investigating rDNA regulation across population and disease studies. RICO is available at https://github.com/comprna/RICO.