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

Yaqiang CaoiD, Guangzhe GeiD, Keji ZhaoiD

PDFDOICodeData

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 .

In-depth summary

Claude Opus 5.5 · xhigh effort · 8 Nov 2026 · from the full text of version 2 (15 Sept 2026) · CC0 1.0

Ryder normalizes epigenomic bigWig tracks against stable internal reference regions such as invariant CTCF sites, correcting background and signal separately, recovering expected perturbation effects that depth-only normalization obscured.

Technical differences in background and signal-to-noise distort cross-sample comparison of chromatin profiles; per the authors, spike-ins assume hard-to-verify equivalence with endogenous chromatin, and MAnorm, S3norm and IGN depend on invariant shared peaks, clean signal/background separation or matched RNA-seq. Ryder anchors normalization to internal regions whose stability is testable in the data and corrects background and foreground separately instead of applying one global factor.

Key results

  1. ShownIn DNase-seq of Gata3 knockout (KO) versus wild-type DN3 thymocytes, reads-per-million (RPM) normalization showed a global signal rise even at invariant CTCF-associated DNase hypersensitive sites (DHSs). Ryder aligned these reference regions and revealed the expected loss of accessibility at GATA3-bound DHSs.

    KO-increased GATA3-bound DHSs fell from 687 under RPM to 8 after Ryder; KO-decreased ones rose from 73 to 320. S3norm left reference DHSs significantly lower in KO and missed the GATA3-bound differences. Boxplot density comparisons used two-sided paired Wilcoxon signed-rank tests across matched regions; the site counts come from a fold-change ≥ 2 threshold on MA plots. · 3.1 Ryder normalizes epigenomic signal tracks…; 3.2 Ryder corrects technical signal inflation in…; Figure legends

  2. ShownAcross three BRG1-loss datasets (auxin-inducible degron DNase-seq in mouse fibroblasts, dTAG13 titration CUT&RUN and ATAC-seq in mouse embryonic stem cells (mESCs), and inhibitor-treated human MV411 ATAC-seq), Ryder showed enhancer-preferential loss of BRG1 binding or accessibility, with promoters less affected.

    In the dTAG series, RPM-normalized CUT&RUN carried a background shift, whereas Ryder showed dose-dependent loss of BRG1 binding and of ATAC-seq accessibility at enhancers; promoter binding changed only under extensive degradation. In MV411 (AU15330, BRM014), RPM gave a spurious promoter accessibility gain. S3norm missed or weakened the enhancer-specific pattern in all three datasets. · 3.4 DNase-seq analysis of BRG1-AID cells with…; 3.5 Ryder recovers BRG1-dependent enhancer…; Figure legends

  3. SuggestedRyder kept expected global shifts rather than flattening them. H3K27me3 fell after EZH2 inhibition without a spurious H3K4me3 gain, H3K9ac rose after HDAC inhibition, and BRM014 raised MNase-seq nucleosome occupancy at enhancer centres.

    In PC9 cells (DMSO vs GSK126), RPM normalization produced an apparent H3K4me3 increase that Ryder, using peaks overlapping invariant CTCF sites, avoided. In HeLa-S3 cells (DMSO vs Trichostatin A, with variable yeast spike-in ratios), Ryder showed the anticipated H3K9ac increase. mESC MNase-seq was normalized with the background factor sb alone; flanking occupancy fell and the +1 nucleosome rose at promoters. · 3.6 Ryder preserves expected global changes in…; Figure legends

  4. SuggestedSpike-in normalization did not act as a fixed gold standard. Read-ratio and profile-based spike-in corrections gave different results, and CTCF-referenced Ryder called more decreased sites than spike-in read ratio in the BRG1 degron DNase-seq.

    In BRG1-AID fibroblasts with human 293T spike-in cells, all methods showed reduced enhancer accessibility, but internal-reference normalization found more significantly decreased enhancer and promoter sites than RPM or spike-in ratio. In MV411 ATAC-seq with a Drosophila S2 spike-in, the ratio and profile spike-in strategies diverged, and S2 and MV411 signal at the displayed loci looked non-equivalent. · 3.4 DNase-seq analysis of BRG1-AID cells with…; 3.5 Ryder recovers BRG1-dependent enhancer…; Figure legends

  5. SuggestedIn ATAC-seq of resting versus stimulated CD8+ T cells, peaks at transcription start sites (TSSs) of stably expressed genes, chosen from matched RNA-seq, worked as a reference set in place of CTCF sites. They kept the activation-linked global gain in accessibility.

    The TSS references came from 500 genes with the smallest fold changes and >1 RPKM in both conditions. With either reference set, accessibility rose significantly at TSSs of upregulated genes. At Ifng, RPM showed an apparent post-stimulation drop at the promoter and nearby enhancers, whereas Ryder preserved the gain that matches the gene's induction. · 3.3 Stable TSSs can serve as alternative…

Methods, data and reproducibility

paw.py takes depth-normalized (RPM/CPM) control and target bigWigs, drops outlier reference regions by Mahalanobis distance in M-A space and samples flanking background. It then estimates background and foreground scale factors, log2 alignment parameters and a foreground/background cutoff, applied genome-wide in two tiers. patrol.py then flags differential regions by fold change, Poisson or Mahalanobis criteria.

  • DNase-seq, wild-type vs tamoxifen-induced Gata3 knockout DN3 thymocytes (generated in this study) · mouse · n = two biological replicates · GSE300647
  • DNase-seq, control vs BRG1-AID ear fibroblasts with human 293T spike-in cells (generated in this study) · mouse (human 293T spike-in) · GSE300647
  • BRG1 CUT&RUN and ATAC-seq across a dTAG13 degrader titration in mESCs (public, reanalysed) · mouse
  • ATAC-seq of MV411 cells treated with DMSO, AU15330 or BRM014, Drosophila S2 spike-in (public) · human (Drosophila S2 spike-in)
  • ATAC-seq plus matched RNA-seq of resting vs stimulated CD8+ T cells (public; previously used for IGN)
  • MNase-seq of mESCs treated with DMSO or BRM014 (public) · mouse
  • H3K4me3 and H3K27me3 ChIP-seq in PC9 cells, DMSO vs EZH2 inhibitor GSK126, with spike-in (public) · human
  • H3K9ac ChIP-seq in HeLa-S3 cells, DMSO vs Trichostatin A, variable yeast spike-in ratios (public) · human
Methods & tools
Ryder (paw.py, patrol.py), Bowtie2 v2.3.5, cLoops2, deepTools2 bigwigCompare, BEDtools intersectBed, S3norm, Cistrome, ENCODE blacklist, GENCODE M21/V30, UCSC Genome Browser, auxin-inducible degron (AID), dTAG
Software & environment
Python package; the exact version used is archived on Zenodo (DOI 10.5281/zenodo.21267457); Bowtie2 v2.3.5; mm10/hg38 with GENCODE M21/V30.
Assay conditions & compute
Ryder needed less runtime and memory than S3norm on the Gata3 DNase-seq benchmark (Figure S1F); hardware and absolute values are not given in the text.
Reproducibility gaps
Accessions for the reanalysed public datasets appear only in Supplementary Table S6; no dependency list, container or requirements file is described in the text.
Limitations (6)
  • PATROL's differential calls (fold change, Poisson or Mahalanobis) do not model biological replicate variance. Formal replicate-aware testing should run DESeq2 or edgeR on per-replicate Ryder signals or counts. · stated by the authors
  • Not meant for individual sparse single-cell epigenomic profiles. It can be used on pseudo-bulk groups only when coverage is adequate, because low coverage makes the foreground and background estimates unstable. · stated by the authors
  • Depends on reference regions staying stable. CTCF sites are inappropriate when the perturbation acts on CTCF, and any reference sites the perturbation affects must be removed first. · stated by the authors
  • The default flank shifts used for background may fail for broad or diffuse marks. Users must inspect the noise-cutoff plot and may need another reference set or a single-tier mode. · stated by the authors
  • Accuracy is judged against expected biology (GATA3, BRG1, EZH2 and HDAC effects) rather than against data with known true differential regions. · noted in this summary
  • S3norm is the only computational method compared head-to-head; MAnorm and IGN are discussed but not run. Several key comparisons appear only in supplementary figures. · noted in this summary

Read this if you compare DNase-seq, ATAC-seq, CUT&RUN or ChIP-seq tracks across perturbations that may shift signal globally, and want a testable alternative or cross-check to spike-in and RPM normalization.

Citations
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