Fanchon, E. | Search bioRxiv

bioRxiv2025

Pylluminator: fast and scalable analysis of DNA methylation data in Python

MotivationIllumina Infinium BeadChip technology for DNA methylation analysis continues to expand, with the latest EPICv2 arrays targeting about a million loci. As data volumes from this technology continue to grow, there is increasing demand for more scalable data processing solutions. Meanwhile, Python has gained significant interest in bioinformatics for its efficiency, versatility, and widespread use in data science and machine learning. Yet, no comprehensive Python toolkit exists for Illumina methylation array analysis. ResultsWe present Pylluminator, a Python implementation of essential analysis methods including pre-processing tools, quality control, differential methylation analysis, and visualizations. Based on the established R packages SeSAMe and ChAMP, Pylluminator provides a scalable, user-friendly toolkit for DNA methylation analysis. Availability and implementationPylluminator is an open-source package under MIT license available at https://github.com/eliopato/pylluminator. It was developed using Python 3.12 and can be installed with pip. The documentation with thorough installation instructions and examples can be found at https://pylluminator.readthedocs.io

bioinformatics↗

bioRxiv2021

Dynamical modeling of the H3K27 epigenetic landscape in mouse embryonic stem cells

The Polycomb system via the methylation of the lysine 27 of histone H3 (H3K27) plays central roles in the silencing of many lineage-specific genes during development. Recent experimental evidence suggested that the recruitment of histone modifying enzymes like the Polycomb repressive complex 2 (PRC2) at specific sites and their spreading capacities from these sites are key to the establishment and maintenance of a proper epigenomic landscape around Polycomb-target genes. Here, to test whether such mechanisms, as a minimal set of qualitative rules, are quantitatively compatible with data, we developed a mathematical model that can predict the locus-specific distributions of H3K27 modifications based on previous biochemical knowledge. Within the biological context of mouse embryonic stem cells, our model showed quantitative agreement with experimental profiles of H3K27 acetylation and methylation around Polycomb-target genes in wild-type and mutants. In particular, we demonstrated the key role of the reader-writer module of PRC2 and of the competition between the binding of activating and repressing enzymes in shaping the H3K27 landscape around transcriptional start sites. The predicted dynamics of establishment and maintenance of the repressive trimethylated H3K27 state suggest a slow accumulation, in perfect agreement with experiments. Our approach represents a first step towards a quantitative description of PcG regulation in various cellular contexts and provides a generic framework to better characterize epigenetic regulation in normal or disease situations.

systems biology↗