This article has been reviewed according to Science X's editorial process and policies. Editors have highlighted the following attributes while ensuring the content's credibility: In a study recently published in Nature Communications, researchers from Berlin, Potsdam, and Jena present a new method for analyzing the epigenome. The machine-learning method identifies differentially methylated DNA regions without sample labels—a prerequisite for many existing algorithms.
This makes it possible to identify previously hidden biological patterns as well as new subgroups of cells or diseases. The activity of our genes is not determined by DNA sequence alone. The attachment of small chemical compounds—known as methyl groups—influences which genes are active and which remain silenced.
DNA methylation is thus a central component of the epigenome. Changes to the epigenome play a crucial role in the development of our bodies, influence the aging process and are relevant to numerous diseases, such as cancer. To understand such changes, researchers specifically search for differentially methylated DNA regions (DMRs).
However, existing methods usually require samples under investigation to be assigned to known groups—such as healthy or diseased tissue. With complex clinical datasets, however, this information is often unknown. With metilene3, a new software tool, researchers have developed a method that overcomes this limitation.
The software can compare DNA methylation patterns both between predefined groups (supervised mode) and among unlabeled samples (unsupervised mode). In the latter mode, the software searches for DMRs without samples having to be preclassified into groups such as "healthy" or "diseased." The software autonomously segments the genome based on methylation signals, grouping the samples automatically. This classification makes it possible to visualize epigenetic similarities and developmental relationships between samples.
Previously unknown cell types or disease subgroups can thus be identified, as well as regions in which samples both resemble and differ from already known diseases or cell types. At the same time, the biological differences remain traceable because every similarity and difference can be attributed to specific methylation patterns. The researchers then tested the method on various biological datasets.
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