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Charting differentially methylated regions in cancer with Rocker-meth.


ABSTRACT: Differentially DNA methylated regions (DMRs) inform on the role of epigenetic changes in cancer. We present Rocker-meth, a new computational method exploiting a heterogeneous hidden Markov model to detect DMRs across multiple experimental platforms. Through an extensive comparative study, we first demonstrate Rocker-meth excellent performance on synthetic data. Its application to more than 6,000 methylation profiles across 14 tumor types provides a comprehensive catalog of tumor type-specific and shared DMRs, and agnostically identifies cancer-related partially methylated domains (PMD). 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. Finally, we demonstrate the utility of the catalog for the study of colorectal cancer single-cell DNA-methylation data.

SUBMITTER: Benelli M 

PROVIDER: S-EPMC8563962 | biostudies-literature | 2021 Nov

REPOSITORIES: biostudies-literature

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Charting differentially methylated regions in cancer with Rocker-meth.

Benelli Matteo M   Franceschini Gian Marco GM   Magi Alberto A   Romagnoli Dario D   Biagioni Chiara C   Migliaccio Ilenia I   Malorni Luca L   Demichelis Francesca F  

Communications biology 20211102 1


Differentially DNA methylated regions (DMRs) inform on the role of epigenetic changes in cancer. We present Rocker-meth, a new computational method exploiting a heterogeneous hidden Markov model to detect DMRs across multiple experimental platforms. Through an extensive comparative study, we first demonstrate Rocker-meth excellent performance on synthetic data. Its application to more than 6,000 methylation profiles across 14 tumor types provides a comprehensive catalog of tumor type-specific an  ...[more]

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