MeinteR: A framework to prioritize DNA methylation aberrations based on conformational and cis-regulatory element enrichment.
Title | MeinteR: A framework to prioritize DNA methylation aberrations based on conformational and cis-regulatory element enrichment. |
Publication Type | Journal Article |
Year of Publication | 2019 |
Authors | Malousi, A., Kouidou S., Tsagiopoulou M., Papakonstantinou N., Bouras E., Georgiou E., Tzimagiorgis G., & Stamatopoulos K. |
Journal | Sci Rep |
Volume | 9 |
Issue | 1 |
Pagination | 19148 |
Date Published | 2019 12 16 |
ISSN | 2045-2322 |
Keywords | Animals, Breast Neoplasms, Carcinoma, Hepatocellular, Databases, Genetic, DNA Methylation, Epigenesis, Genetic, Female, G-Quadruplexes, Gene Expression Regulation, Neoplastic, Genome, Human, Genome-Wide Association Study, Humans, Liver Neoplasms, Mice, Mutation, Nucleic Acid Conformation, Rats, Regulatory Sequences, Nucleic Acid, Software, Workflow |
Abstract | DNA methylation studies have been reformed with the advent of single-base resolution arrays and bisulfite sequencing methods, enabling deeper investigation of methylation-mediated mechanisms. In addition to these advancements, numerous bioinformatics tools address important computational challenges, covering DNA methylation calling up to multi-modal interpretative analyses. However, contrary to the analytical frameworks that detect driver mutational signatures, the identification of putatively actionable epigenetic events remains an unmet need. The present work describes a novel computational framework, called MeinteR, that prioritizes critical DNA methylation events based on the following hypothesis: critical aberrations of DNA methylation more likely occur on a genomic substrate that is enriched in cis-acting regulatory elements with distinct structural characteristics, rather than in genomic "deserts". In this context, the framework incorporates functional cis-elements, e.g. transcription factor binding sites, tentative splice sites, as well as conformational features, such as G-quadruplexes and palindromes, to identify critical epigenetic aberrations with potential implications on transcriptional regulation. The evaluation on multiple, public cancer datasets revealed significant associations between the highest-ranking loci with gene expression and known driver genes, enabling for the first time the computational identification of high impact epigenetic changes based on high-throughput DNA methylation data. |
DOI | 10.1038/s41598-019-55453-8 |
Alternate Journal | Sci Rep |
PubMed ID | 31844073 |
PubMed Central ID | PMC6915744 |