Branchpoint Prediction Using Self-Attention Based Deep Neural Networks

Hyeonseok Lee, Sungchan Kim

http://doi.org/10.5626/JOK.2020.47.4.343

Splicing is a ribonucleic acid (RNA) process of creating a messenger RNA (mRNA) translated into proteins. Branchpoints are sequence elements of RNAs essential in splicing. This paper proposes a novel method for branchpoint prediction. Identification of branchpoints involves several challenges. Branchpoint sites are known to depend on several sequence patterns, called motifs. Also, a branchpoint distribution is highly biased, imposing a class-imbalanced problem. Existing approaches are limited in that they either rely on handcrafted sequential features or ignore the class imbalance. To address those difficulties, the proposed method incorporates 1) Attention mechanisms to learn sequence-positional long-term dependencies, and 2) Regularization with triplet loss to alleviate the class imbalance. Our method is comparable to the state-of-the-art performance while providing rich interpretability on its decisions.