GuidingNet: revealing transcriptional cofactor and predicting binding for DNA methyltransferase by network regularization

Abstract

The DNA methyltransferases (DNMTs) (DNMT3A, DNMT3B and DNMT3L) are primarily responsible for the establishment of genomic locus-specific DNA methylation patterns, which play an important role in gene regulation and animal development. However, this important protein family's binding mechanism, i.e. how and where the DNMTs bind to genome, is still missing in most tissues and cell lines. This motivates us to explore DNMTs and TF's cooperation and develop a network regularized logistic regression model, GuidingNet, to predict DNMTs' genome-wide binding by integrating gene expression, chromatin accessibility, sequence and protein-protein interaction data. GuidingNet accurately predicted methylation experimental data validated DNMTs' binding, outperformed single data source based and sparsity regularized methods and performed well in within and across tissue prediction for several DNMTs in human and mouse. Importantly, GuidingNet can reveal transcription cofactors assisting DNMTs for methylation establishment. This provides biological understanding in the DNMTs' binding specificity in different tissues and demonstrate the advantage of network regularization. In addition to DNMTs, GuidingNet achieves good performance for other chromatin regulators' binding. GuidingNet is freely available at https://github.com/AMSSwanglab/GuidingNet.

Keywords: DNA methyltransferase; data integration; network regularization; transcription cofactor.