Dataset Information
SEMplMe: a tool for integrating DNA methylation effects in transcription factor binding affinity predictions.
Ontology highlight
ABSTRACT: Motivation
Aberrant DNA methylation in transcription factor binding sites has been shown to lead to anomalous gene regulation that is strongly associated with human disease. However, the majority of methylation-sensitive positions within transcription factor binding sites remain unknown. Here we introduce SEMplMe, a computational tool to generate predictions of the effect of methylation on transcription factor binding strength in every position within a transcription factor's motif.Results
SEMplMe uses ChIP-seq and whole genome bisulfite sequencing to predict effects of methylation within binding sites. SEMplMe validates known methylation sensitive and insensitive positions within a binding motif, identifies cell type specific transcription factor binding driven by methylation, and outperforms SELEX-based predictions for CTCF. These predictions can be used to identify aberrant sites of DNA methylation contributing to human disease.Availability and implementation
SEMplMe is available from https://github.com/Boyle-Lab/SEMplMe .
SUBMITTER: Nishizaki SS
PROVIDER: S-EPMC9351228 | biostudies-literature | 2022 Aug
REPOSITORIES: biostudies-literature
Publications
SEMplMe: a tool for integrating DNA methylation effects in transcription factor binding affinity predictions.
BMC bioinformatics 20220804 1
<h4>Motivation</h4>Aberrant DNA methylation in transcription factor binding sites has been shown to lead to anomalous gene regulation that is strongly associated with human disease. However, the majority of methylation-sensitive positions within transcription factor binding sites remain unknown. Here we introduce SEMplMe, a computational tool to generate predictions of the effect of methylation on transcription factor binding strength in every position within a transcription factor's motif.<h4>R ...[more]