Transcription factors (TFs) are proteins that facilitate the transcription of DNA into RNA. A number of recent studies have observed that the binding of TFs onto DNA can be affected by DNA methylation, and in turn, DNA methylation can also be added or removed by proteins associated with transcription factors (Bonder et al. 2017; Banovich et al. 2014; Zhu, Wang, and Qian 2016).
To provide functional annotations for differentially methylated regions (DMRs)
and differentially methylated CpG sites (DMS),
MethReg performs integrative
analyses using matched DNA methylation and gene expression along with
Transcription Factor Binding Sites (TFBS) data. MethReg evaluates, prioritizes
and annotates DNA methylation regions (or sites) with high regulatory potential
that works synergistically with TFs to regulate target gene expressions,
without any additional ChIP-seq data.
The results from
MethReg can be used to generate testable hypothesis on the
synergistic collaboration of DNA methylation changes and TFs in gene regulation.
MethReg can be used either to evaluate regulatory potentials of candidate
regions or to search for methylation coupled TF regulatory processes in the entire genome.
MethReg is a Bioconductor package and can be installed through
if (!"BiocManager" %in% rownames(installed.packages())) install.packages("BiocManager") BiocManager::install("MethReg", dependencies = TRUE)
After the package is installed, it can be loaded into R workspace by
The figure below illustrates the workflow for MethReg. Given matched array DNA methylation data and RNA-seq gene expression data, MethReg additionally incorporates TF binding information from ReMap2020 (Chèneby et al. 2019) or the JASPAR2020 (Baranasic 2020; Fornes et al. 2020) database, and optionally additional TF-target gene interaction databases, to perform both promoter and distal (enhancer) analysis.
In the unsupervised mode, MethReg analyzes all CpGs on the Illumina arrays. In the supervised mode, MethReg analyzes and prioritizes differentially methylated CpGs identified in EWAS.
There are three main steps: (1) create a dataset with triplets of CpGs, TFs that bind near the CpGs, and putative target genes, (2) for each triplet (CpG, TF, target gene), apply integrative statistical models to DNA methylation, target gene expression, and TF expression values, and (3) visualize and interpret results from statistical models to estimate individual and joint impacts of DNA methylation and TF on target gene expression, as well as annotate the roles of TF and CpG methylation in each triplet.
The results from the statistical models will also allow us to identify a list of CpGs that work synergistically with TFs to influence target gene expression.