Deep learning deciphers the related role of master regulators and G-quadruplexes in tissue specification

Scientific Reports. 2025. Vol. 15. Article 23119.

Башкатов А. Б., Andreasyan A., Коновалов Д. Л., Herbert A., Попцова М. С.

G-quadruplexes (GQs) are non-canonical DNA structures encoded by G-flipons with potential roles in gene regulation and chromatin structure. Here, we explore the role of G-flipons in tissue specification. We present a deep learning-based framework for the genome-wide G-flipon predictions across 14 human tissue types. The model was trained using high-confidence experimental maps of GQ-forming sequences and ATAC-seq peaks, conjoined with the location of RNA polymerase, histone marks, and transcription factor binding sites. The training dataset for the DeepGQ model was derived from EndoQuad level 4–6 GQs. Model predictions were subsequently validated against the comprehensive EndoQuad dataset (levels 1–6) to optimize the whole-genome prediction threshold. To identify tissue-specific regulatory patterns, we classified GQ promoter predictions as either ‘core’ or ‘tissue-specific’. We identified a notable overlap between predicted unique tissue-specific GQ sites and master regulatory genes (MRGs), tissue-specific DNase-hypersensitivity sites, and proteins that modulate R-loop formation. Collectively, the findings highlight the transactions between MRG and G-flipons intermediated by RNA: DNA hybrids associated with tissue specification.

Ключевые слова: deep learning, genome regulation, quadruplexes, RNN, non-B DNA, CNN, G-quadruplex, non-B DNA structures, flipon, machine learning