Cover Image

MethylBERT enables read-level DNA methylation pattern identification and tumour deconvolution using a Transformer-based model

DNA methylation (DNAm) is a key epigenetic mark that shows profound alterations in cancer. Read-level methylomes enable more in-depth analyses, due to their broad genomic coverage and preservation of rare cell-type signals, compared to summarized data such as 450K/EPIC microarrays. Here, we propose...

Full description

Saved in:
Bibliographic Details
Main Authors: Jeong, Yunhee (Author) , Gerhäuser, Clarissa (Author) , Sauter, Guido (Author) , Schlomm, Thorsten (Author) , Rohr, Karl (Author) , Lutsik, Pavlo (Author)
Format: Article (Journal)
Language:English
Published: 17 January 2025
In: Nature Communications
Year: 2025, Volume: 16, Pages: 1-14
ISSN:2041-1723
DOI:10.1038/s41467-025-55920-z
Online Access:Verlag, kostenfrei, Volltext: https://doi.org/10.1038/s41467-025-55920-z
Verlag, kostenfrei, Volltext: https://www.nature.com/articles/s41467-025-55920-z
Get full text
Author Notes:Yunhee Jeong, Clarissa Gerhäuser, Guido Sauter, Thorsten Schlomm, Karl Rohr, and Pavlo Lutsik
Description
Summary:DNA methylation (DNAm) is a key epigenetic mark that shows profound alterations in cancer. Read-level methylomes enable more in-depth analyses, due to their broad genomic coverage and preservation of rare cell-type signals, compared to summarized data such as 450K/EPIC microarrays. Here, we propose MethylBERT, a Transformer-based model for read-level methylation pattern classification. MethylBERT identifies tumour-derived sequence reads based on their methylation patterns and local genomic sequence, and estimates tumour cell fractions within bulk samples. In our evaluation, MethylBERT outperforms existing deconvolution methods and demonstrates high accuracy regardless of methylation pattern complexity, read length and read coverage. Moreover, we show its applicability to cell-type deconvolution as well as non-invasive early cancer diagnostics using liquid biopsy samples. MethylBERT represents a significant advancement in read-level methylome analysis and enables accurate tumour purity estimation. The broad applicability of MethylBERT will enhance studies on both tumour and non-cancerous bulk methylomes.
Item Description:Gesehen am 18.08.2025
Physical Description:Online Resource
ISSN:2041-1723
DOI:10.1038/s41467-025-55920-z

Similar Items