DeepLocRNA: An Interpretable Deep Learning Model for Predicting RNA Subcellular Localization with domain-specific transfer-learning

Abstract

Accurate prediction of RNA subcellular localization plays an important role in understanding cellular processes and functions. Although post-transcriptional processes are governed by trans-acting RNA-binding proteins (RBPs) through interaction with cis-regulatory RNA motifs, current methods do not incorporate RBP-binding information. In this paper, we propose DeepLocRNA, an interpretable deep-learning model that leverages a pre-trained multi-task RBP-binding prediction model to predict the subcellular localisation of RNA molecules via fine-tuning. We constructed DeepLocRNA using a comprehensive dataset with variant RNA types and evaluated it on held-out RNA species. Our model achieved state-of-the-art performance in predicting RNA subcellular localization in mRNA and miRNA. It has demonstrated great generalization capabilities, not only for human RNA but also for mice. Moreover, the interpretability of the model is enhanced through the motif analysis, enabling the understanding of the signal factors that contribute to the predictions. The proposed model provides general and powerful prediction abilities for different RNA and species, offering valuable insights into the localisation patterns of RNA molecules and contributing to advancing our understanding of cellular processes at the molecular level.