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

ABSTRACT: 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

SUBMITTER: Bashkatov A 

PROVIDER: S-EPMC12222854 | biostudies-literature | 2025 Jul

REPOSITORIES: biostudies-literature