Ryder: epigenome normalization using a two-tier model and internal reference regions
- 1Division of Intramural Research, Laboratory of Epigenome Biology, Systems Biology Center, National Heart, Lung, and Blood Institute, National Institutes of Health , Bethesda, MD 20892,
Abstract
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 of epigenomic signal tracks. Ryder leverages stable internal reference regions, such as invariant CTCF binding sites, to correct for technical artifacts genome-wide. Our results show that it effectively adjusts both background noise and signal intensity, ensuring accurate signal alignment across samples while preserving genuine biological differences. We demonstrate that Ryder performs robustly across diverse assays including DNase-seq, CUT&RUN, ATAC-seq, MNase-seq, and ChIP-seq, with or without spike-in controls. By reducing technical noise, 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, documentation and test data are freely available at: https://github.com/YaqiangCao/ryder. The software version used in this study is archived at Zenodo: https://zenodo.org/records/21267457.
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BibTeX
@article{cao2026ryder,
title = {{Ryder: epigenome normalization using a two-tier model and internal reference regions}},
author = {Yaqiang Cao and Guangzhe Ge and Keji Zhao},
journal = {Bioinformatics},
year = {2026},
month = oct,
doi = {10.1093/bioinformatics/btag753},
url = {https://academic.oup.com/bioinformatics/article/doi/10.1093/bioinformatics/btag753/8917313},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - Ryder: epigenome normalization using a two-tier model and internal reference regions
AU - Yaqiang Cao
AU - Guangzhe Ge
AU - Keji Zhao
PY - 2026
DA - 2026/10/15
JO - Bioinformatics
DO - 10.1093/bioinformatics/btag753
UR - https://academic.oup.com/bioinformatics/article/doi/10.1093/bioinformatics/btag753/8917313
AB - 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 of epigenomic signal tracks. Ryder leverages stable internal reference regions, such as invariant CTCF binding sites, to correct for technical artifacts genome-wide. Our results show that it effectively adjusts both background noise and signal intensity, ensuring accurate signal alignment across samples while preserving genuine biological differences. We demonstrate that Ryder performs robustly across diverse assays including DNase-seq, CUT&RUN, ATAC-seq, MNase-seq, and ChIP-seq, with or without spike-in controls. By reducing technical noise, 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, documentation and test data are freely available at: https://github.com/YaqiangCao/ryder. The software version used in this study is archived at Zenodo: https://zenodo.org/records/21267457.
SN - 1367-4803
SN - 1367-4811
ER -