iDualG4: A Dual-Channel Deep Learning Framework for Predicting In Vivo G-Quadruplexes

G-quadruplexes (G4s) are non-canonical nucleic acid secondary structures that help maintain genomic stability and regulate gene transcription. Although the genome contains a vast number of putative G4-forming sequences (PQSs, sequences with intrinsic in vitro G4-forming potential), only a small fraction fold stably into G4 structures within the complex chromatin environment of living cells. Existing deep learning approaches improve predictive accuracy by incorporating cell line-specific epigenetic data; however, their heavy reliance on costly, large-scale sequencing assays (e.g., ChIP-seq) limits broader application to clinical samples and newly profiled cell lines. To address this challenge, we propose iDualG4, an interpretable dual-channel deep learning framework that uses DNA sequence as the only input at inference time. By leveraging a pretrained Enformer module, iDualG4 infers epigenomic proxy features directly from DNA sequence and integrates them with local sequence features, thereby replacing the need for newly measured cell-specific epigenomic assays during prediction. Evaluations across multiple cell lines, including K562, demonstrate that iDualG4 significantly outperforms existing methods, particularly in handling imbalanced data (achieving an AUPR of 0.981 on K562). Interpretability analysis based on DeepSHAP indicates that iDualG4 provides an in vivo G4 prediction tool combining high precision and interpretability without the need for additional experimental sequencing data, and offers a novel computational framework for elucidating how sequence and the epigenetic environment jointly determine genomic G4 formation.