ArticleBioinformatics advances2026
Pylluminator: fast and scalable analysis of DNA methylation data in Python.
Article in Bioinformatics advances, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Authors and funding
5 authors.
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Abstract
Motivation: Illumina 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. Results: We 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 implementation: Pylluminator 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 and uv. The documentation with thorough installation instructions and examples can be found at https://pylluminator.readthedocs.io.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.