Predicting differentially methylated cytosines in TET and DNMT3 knockout mutants via a large language model.

ABSTRACT: DNA methylation is an epigenetic marker that directly or indirectly regulates several critical cellular processes. While cytosines in mammalian genomes generally maintain stable methylation patterns over time, other cytosines that belong to specific regulatory regions, such as promoters and enhancers, can exhibit dynamic changes. These changes in methylation are driven by a complex cellular machinery, in which the enzymes DNMT3 and TET play key roles. The objective of this study is to design a machine learning model capable of accurately predicting which cytosines have a fluctuating methylation level [hereafter called differentially methylated cytosines (DMCs)] from the surrounding DNA sequence. Here, we introduce L-MAP, a transformer-based large language model that is trained on DNMT3-kno

SUBMITTER: Sereshki S 

PROVIDER: S-EPMC11904404 | biostudies-literature | 2025 Mar

REPOSITORIES: biostudies-literature