MeinteR: A framework to prioritize DNA methylation aberrations based on conformational and cis-regulatory element enrichment.
Animals
Breast Neoplasms
/ genetics
Carcinoma, Hepatocellular
/ genetics
DNA Methylation
/ genetics
Databases, Genetic
Epigenesis, Genetic
Female
G-Quadruplexes
Gene Expression Regulation, Neoplastic
Genome, Human
Genome-Wide Association Study
Humans
Liver Neoplasms
/ genetics
Mice
Mutation
/ genetics
Nucleic Acid Conformation
Rats
Regulatory Sequences, Nucleic Acid
/ genetics
Software
Workflow
Journal
Scientific reports
ISSN: 2045-2322
Titre abrégé: Sci Rep
Pays: England
ID NLM: 101563288
Informations de publication
Date de publication:
16 12 2019
16 12 2019
Historique:
received:
01
07
2019
accepted:
19
11
2019
entrez:
18
12
2019
pubmed:
18
12
2019
medline:
6
11
2020
Statut:
epublish
Résumé
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.
Identifiants
pubmed: 31844073
doi: 10.1038/s41598-019-55453-8
pii: 10.1038/s41598-019-55453-8
pmc: PMC6915744
doi:
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
19148Références
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