CpGmotifs: a tool to discover DNA motifs associated to CpG methylation events

BackgroundThe investigation of molecular alterations associated with the conservation and variation of DNA methylation in eukaryotes is gaining interest in the biomedical research community. Among the different determinants of methylation stability, the DNA composition of the CpG surrounding regions has been shown to have a crucial role in the maintenance and establishment of methylation statuses. This aspect has been previously characterized in a quantitative manner by inspecting the nucleotidic composition in the region. Research in this field still lacks a qualitative perspective, linked to the identification of certain sequences (or DNA motifs) related to particular DNA methylation phenomena.ResultsHere we present a novel computational strategy based on short DNA motif discovery in order to characterize sequence patterns related to aberrant CpG methylation events. We provide our framework as a user-friendly, shiny-based application, CpGmotifs, to easily retrieve and characterize DNA patterns related to CpG methylation in the human genome. Our tool supports the functional interpretation of deregulated methylation events by predicting transcription factors binding sites (TFBS) encompassing the identified motifs.ConclusionsCpGmotifs is an open source software. Its source code is available on GitHub https://github.com/Greco-Lab/CpGmotifs and a ready-to-use docker image is provided on DockerHub at https://hub.docker.com/r/grecolab/cpgmotifs.

Automated summary

This study presents a novel computational strategy based on short DNA motif discovery to explore sequence patterns related to aberrant CpG methylation events and provides a user-friendly application, CpGmotifs, for identifying and characterizing DNA patterns associated with CpG methylation in the human genome. The tool supports the functional interpretation of deregulated methylation events by predicting transcription factors binding sites encompassing the identified motifs.