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

Area of Science:

• Epigenomics
• Bioinformatics
• Computational Biology

Background:

• Sequencing-based epigenomic profiling methods are powerful tools for biological research.
• Technical variability in these methods complicates cross-sample comparisons and can obscure true biological signals.
• Existing normalization methods often rely on assumptions that may not hold across diverse experimental conditions or require additional data types.

Purpose of the Study:

• To present Ryder, a flexible and robust Python package for the normalization and differential analysis of epigenomic data.
• To introduce a novel normalization strategy that leverages stable internal reference regions to correct for technical artifacts genome-wide.
• To demonstrate the package's effectiveness across a range of epigenomic assays.

Main Methods:

• Ryder utilizes stable internal reference regions, such as invariant CTCF binding sites, for normalization.
• The package models and adjusts for both background noise and signal intensity.
• It performs genome-wide normalization, correcting signals in both peak and background regions.

Main Results:

• Ryder effectively corrects technical artifacts genome-wide across diverse assays including DNase-seq, CUT&RUN, ATAC-seq, MNase-seq, and ChIP-seq.
• The normalization strategy is robust and does not require spike-in controls.
• By reducing technical noise, Ryder improves the detection of genuine biological changes, exemplified by enhanced detection of chromatin accessibility alterations.

Conclusions:

• Ryder offers a flexible and robust solution for normalizing epigenomic data, enhancing the accuracy of biological signal detection.
• The package's ability to perform genome-wide normalization across various assays makes it a valuable tool for epigenomic research.
• Ryder's approach improves the identification of biologically relevant changes by effectively mitigating technical variability.