Zone Equalisation Normalisation for Improved Alignment of Epigenetic Signal

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ID: 325883
2026
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Abstract
MOTIVATION: High-throughput genomic technologies have transformed our understanding of biological systems, yet direct comparison and visualisation of these complex datasets remains challenging. Existing normalisation methods often fail to align genomic signal across samples due to sensitivity to sequencing depth differences and localised high-signal artefacts, leading to inconsistent replicate behaviour and increased downstream variability. RESULTS: We introduce Zone Equalisation Normalisation (ZEN), a novel approach designed to improve cross-sample signal alignment of genomic data. ZEN rescales genomic signal based on variance estimated within biologically enriched regions, reducing the influence of extreme outliers while preserving underlying biological structure. Using a diverse collection of data and our new genome-wide benchmarking approach, we reveal that ZEN improves biological and technical replicate alignment across the majority of tested conditions and experimental platforms. We further show that this improved signal comparability is associated with fewer differential accessibility calls between technical replicates and a more conservative set of biological differences. Together, these results demonstrate that ZEN provides a complementary framework to improve the accuracy and reliability of genomic data analysis and that normalisation choice can affect downstream analyses and biological interpretation. AVAILABILITY AND IMPLEMENTATION: ZEN is available as an open-source Python package via conda and PyPI. Source code, documentation, tutorials, and code to reproduce the analyses are available at https://github.com/Genome-Function-Initiative-Oxford/Zone-Equalisation-Normalisation and Zenodo (https://doi.org/10.5281/zenodo.21067751). SUPPLEMENTARY INFORMATION: Supplementary data are available with the manuscript.
Reference Key
openalex_W4417291839 Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Tom Wilson, Thomas A. Milne, Simone G. Riva, Jim R. Hughes
Journal BMC Bioinformatics
Year 2026
DOI
10.1093/bioinformatics/btag622
URL
https://doi.org/10.1093/bioinformatics/btag622
Keywords Keywords not found

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