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January 1, 2026Physical Chemistry Chemical Physics

TransG4: an interpretable deep-learning approach for sequence-based G-quadruplex prediction

Yongna Yuan Yaojie Tian Zhenyu Liu J. Ma

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Key Points

This research aims to develop a deep learning model to predict G-quadruplex formation from DNA and RNA sequences.
Utilized CNN, transformer, and BiGRU models for prediction
Analyzed G4-seq and rG4-seq data
Estimated DNA mismatch and RNA RSR scores
Identified sequence features using attention-derived motifs
Successfully predicted G-quadruplex formation propensity
Revealed sequence features associated with G4 structures through attention mechanisms
Estimated mismatch and RSR scores average and variability in significant sequences

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Implication

Deep learning predicts G-quadruplex formation using sequence data, suggesting new insights into DNA and RNA structures.

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Cite This Study

Yuan et al. (2026) studied this question.

synapsesocial.com/papers/69c620be15a0a509bde195cf https://doi.org/10.1039/d6cp00173d

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