ABSTRACT
Differentially DNA methylated regions (DMRs) inform on the role of epigenetic changes in cancer. We present Rocker-meth, a computational method exploiting a heterogeneous hidden Markov model to detect DMRs across multiple experimental platforms. Its application to more than 6,000 methylation profiles across 14 tumor types provides a comprehensive catalog of tumor type-specific and shared DMRs, also amenable to single-cell DNA-methylation data. In depth integrative analysis including orthogonal omics shows the enhanced ability of Rocker-meth in recapitulating known associations, further uncovering the pan-cancer relationship between DNA hypermethylation and transcription factor deregulation depending on the baseline chromatin state.
Competing Interest Statement
The authors have declared no competing interest.
Footnotes
This version has been updated with few clarifications on the advantages and limits of the algorithm.