DeepLocRNA: an interpretable deep learning model for predicting RNA subcellular localization with domain-specific transfer-learning

Wang, Jun1; Horlacher, Marc2; Cheng, Lixin3; Winther, Ole1,4,5

2024-02-01

10.1093/bioinformatics/btae065

BIOINFORMATICS   影响因子和分区

1367-4803

1367-4811

["Motivation 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.Results In this article, we propose DeepLocRNA, an interpretable deep-learning model that leverages a pre-trained multi-task RBP-binding prediction model to predict the subcellular localization of RNA molecules via fine-tuning. We constructed DeepLocRNA using a comprehensive dataset with variant RNA types and evaluated it on the held-out dataset. Our model achieved state-of-the-art performance in predicting RNA subcellular localization in mRNA and miRNA. It has also demonstrated great generalization capabilities, performing well on both human and mouse RNA. Additionally, a motif analysis was performed to enhance the interpretability of the model, highlighting signal factors that contributed to the predictions. The proposed model provides general and powerful prediction abilities for different RNA types and species, offering valuable insights into the localization patterns of RNA molecules and contributing to our understanding of cellular processes at the molecular level. A user-friendly web server is available at: https://biolib.com/KU/DeepLocRNA/.","Graphical Abstract"]

SCI

英语

通讯

Novo Nordisk Fonden[NNF20OC0062606] ; Danish National Research Foundation [the Pioneer Centre for AI][P1]

Biochemistry & Molecular Biology ; Biotechnology & Applied Microbiology ; Computer Science ; Mathematical & Computational Biology ; Mathematics

Biochemical Research Methods ; Biotechnology & Applied Microbiology ; Computer Science, Interdisciplinary Applications ; Mathematical & Computational Biology ; Statistics & Probability

WOS:001166949000007

OXFORD UNIV PRESS

BIOLOGY & BIOCHEMISTRY

Web of Science

1.Univ Copenhagen, Bioinformat Ctr, Dept Biol, DK-2100 Copenhagen O, Denmark
2.Helmholtz Ctr Munich, Computat Hlth Ctr, D-85764 Neuherberg, Germany
3.Southern Univ Sci & Technol, Jinan Univ, Shenzhen Peoples Hosp, Affiliated Hosp 1,Clin Med Coll 2, Shenzhen 518020, Peoples R China
4.Copenhagen Univ Hosp, Ctr Genom Med, Rigshosp, DK-2100 Copenhagen, Denmark
5.Tech Univ Denmark, Dept Appl Math & Comp Sci, Sect Cognit Syst, DK-2800 Lyngby, Denmark

Wang, Jun,Horlacher, Marc,Cheng, Lixin,et al. DeepLocRNA: an interpretable deep learning model for predicting RNA subcellular localization with domain-specific transfer-learning[J]. BIOINFORMATICS,2024,40(2).

Wang, Jun,Horlacher, Marc,Cheng, Lixin,&Winther, Ole.(2024).DeepLocRNA: an interpretable deep learning model for predicting RNA subcellular localization with domain-specific transfer-learning.BIOINFORMATICS,40(2).

Wang, Jun,et al."DeepLocRNA: an interpretable deep learning model for predicting RNA subcellular localization with domain-specific transfer-learning".BIOINFORMATICS 40.2(2024).