Ryder: Epigenome normalization using a two-tier model and internal reference regions.

Motivation: Sequencing-based epigenomic profiling methods are powerful but suffer from technical variability that complicates cross-sample comparisons and can obscure true biological signals. While existing normalization methods using spike-in controls or computational approaches have been proposed, they often rely on assumptions that may not hold across diverse experimental conditions or require additional data types. Results: We present Ryder, a flexible and robust Python package for the normalization and differential analysis of epigenomic data. Ryder introduces a normalization strategy that leverages stable internal reference regions, such as invariant CTCF binding sites, to correct for technical artifacts genome-wide. Our results show that it effectively models and adjusts both background noise and signal intensity, ensuring accurate signal alignment across samples. We demonstrate that Ryder performs robust, genome-wide normalization - correcting signals in both peak and background regions - across a range of assays including DNase-seq, CUT&RUN, ATAC-seq, MNase-seq, and ChIP-seq, with or without spike-in controls. By reducing technical noise, we show that Ryder improves the detection of genuine biological changes, such as quantitative reduction of chromatin accessibility at key enhancer elements by depletion of BRG1, a key subunit of the chromatin remodeling BAF complexes. Availability and Implementation: The Ryder source code and documentation are freely available at: https://github.com/YaqiangCao/ryder .