EnrichRBP: an automated and interpretable computational platform for predicting and analyzing RNA-binding protein events

Yubo Wang; Haoran Zhu; Yansong Wang; Yuning Yang; Yujian Huang; Jian Zhang; Ka-Chun Wong; Xiangtao Li

doi:10.1093/bioinformatics/btaf018

EnrichRBP: an automated and interpretable computational platform for predicting and analyzing RNA-binding protein events

Bioinformatics. 2025 Jan 13:btaf018. doi: 10.1093/bioinformatics/btaf018. Online ahead of print.

Authors

Yubo Wang¹, Haoran Zhu¹, Yansong Wang¹, Yuning Yang², Yujian Huang³, Jian Zhang⁴, Ka-Chun Wong⁵, Xiangtao Li¹

Affiliations

¹ School of Artificial Intelligence, Jilin University, Jilin, China.
² Information Science and Technology, Northeast Normal University, Changchun, China.
³ College of Computer Science and Cyber Security, Chengdu University of Technology, Chengdu, China.
⁴ School of Computer and Information Technology, Xinyang Normal University, Xinyang, 464000, China.
⁵ Department of Computer Science, City University of Hong Kong, Hong Kong SAR.

PMID: 39804669
DOI: 10.1093/bioinformatics/btaf018

Abstract

Motivation: Predicting RNA-binding proteins (RBPs) is central to understanding post-transcriptional regulatory mechanisms. Here, we introduce EnrichRBP, an automated and interpretable computational platform specifically designed for the comprehensive analysis of RBP interactions with RNA.

Results: EnrichRBP is a web service that enables researchers to develop original deep learning and machine learning architectures to explore the complex dynamics of RNA-binding proteins. The platform supports 70 deep learning algorithms, covering feature representation, selection, model training, comparison, optimization, and evaluation, all integrated within an automated pipeline. EnrichRBP is adept at providing comprehensive visualizations, enhancing model interpretability, and facilitating the discovery of functionally significant sequence regions crucial for RBP interactions. In addition, EnrichRBP supports base-level functional annotation tasks, offering explanations and graphical visualizations that confirm the reliability of the predicted RNA binding sites. Leveraging high-performance computing, EnrichRBP provides ultra-fast predictions ranging from seconds to hours, applicable to both pre-trained and custom model scenarios, thus proving its utility in real-world applications. Case studies highlight that EnrichRBP provides robust and interpretable predictions, demonstrating the power of deep learning in the functional analysis of RBP interactions. Finally, EnrichRBP aims to enhance the reproducibility of computational method analyses for RNA-binding protein sequences, as well as reduce the programming and hardware requirements for biologists, thereby offering meaningful functional insights.

Availability and implementation: EnrichRBP is available at https://airbp.aibio-lab.com/. The source code is available at https://github.com/wangyb97/EnrichRBP, and detailed online documentation can be found at https://enrichrbp.readthedocs.io/en/latest/.

Supplementary information: Supplementary data are available at Bioinformatics online.