Machine learning model for sequence-driven DNA G-quadruplex formation

We describe a sequence-based computational model to predict DNA G-quadruplex (G4) formation. The model was developed using large-scale machine learning from an extensive experimental G4-formation dataset, recently obtained for the human genome via G4-seq methodology. Our model differentiates many widely accepted putative quadruplex sequences that do not actually form stable genomic G4 structures, correctly assessing the G4 folding potential of over 700,000 such sequences in the human genome. Moreover, our approach reveals the relative importance of sequence-based features coming from both within the G4 motifs and their flanking regions. The developed model can be applied to any DNA sequence or genome to characterise sequence-driven intramolecular G4 formation propensities.