Prediction of G4 formation in live cells with epigenetic data: a deep learning approach

Abstract

G-quadruplexes (G4s) are secondary structures abundant in DNA that may play regulatory roles in cells. Despite the ubiquity of the putative G-quadruplex sequences (PQS) in the human genome, only a small fraction forms secondary structures in cells. Folded G4, histone methylation and chromatin accessibility are all parts of the complex cis regulatory landscape. We propose an approach for G4 formation prediction in cells that incorporates epigenetic and chromatin accessibility data. The novel approach termed epiG4NN efficiently predicts cell-specific G4 formation in live cells based on a local epigenomic snapshot. Our architecture confirms the close relationship between H3K4me3 histone methylation, chromatin accessibility and G4 structure formation. Trained on A549 cell data, epiG4NN was then able to predict G4x formation in HEK293T and K562 cell lines. We observe the dependency of model performance with different epigenetic features on the underlying experimental condition of G4 detection. We expect that this approach will contribute to the systematic understanding of correlations between structural and epigenomic feature landscape.