G4-Attention: Deep Learning Model with Attention for predicting DNA G-Quadruplexes
<|reference_start|>G4-Attention: Deep Learning Model with Attention for predicting DNA G-Quadruplexes: G-Quadruplexes are the four-stranded non-canonical nucleic acid secondary structures, formed by the stacking arrangement of the guanine tetramers. They are involved in a wide range of biological roles because of their exceptionally unique and distinct structural characteristics. After the completion of the human genome sequencing project, a lot of bioinformatic algorithms were introduced to predict the active G4s regions \textit{in vitro} based on the canonical G4 sequence elements, G-\textit{richness}, and G-\textit{skewness}, as well as the non-canonical sequence features. Recently, sequencing techniques like G4-seq and G4-ChIP-seq were developed to map the G4s \textit{in vitro}, and \textit{in vivo} respectively at a few hundred base resolution. Subsequently, several machine learning approaches were developed for predicting the G4 regions using the existing databases. However, their prediction models were simplistic, and the prediction accuracy was notably poor. In response, here, we propose a novel convolutional neural network with Bi-LSTM and attention layers, named G4-attention, to predict the G4 forming sequences with improved accuracy. G4-attention achieves high accuracy and attains state-of-the-art results in the G4 prediction task. Our model also predicts the G4 regions accurately in the highly class-imbalanced datasets. In addition, the developed model trained on the human genome dataset can be applied to any non-human genome DNA sequences to predict the G4 formation propensities.<|reference_end|>
@article{mukherjee2024g4-attention:,
    title={G4-Attention: Deep Learning Model with Attention for predicting DNA
  G-Quadruplexes},
    author={Shrimon Mukherjee, Pulakesh Pramanik, Partha Basuchowdhuri, Santanu
  Bhattacharya},
    journal={arXiv preprint arXiv:2403.02765},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.02765},
    primaryClass={cs.LG q-bio.BM}
}