Attention-based multi-label neural networks for integrated prediction and interpretation of twelve widely occurring RNA modifications



Song, Zitao, Huang, Daiyun, Song, Bowen, Chen, Kunqi, Song, Yiyou, Liu, Gang, Su, Jionglong, de Magalhaes, Joao Pedro ORCID: 0000-0002-6363-2465, Rigden, Daniel J ORCID: 0000-0002-7565-8937 and Meng, Jia ORCID: 0000-0003-3455-205X
(2021) Attention-based multi-label neural networks for integrated prediction and interpretation of twelve widely occurring RNA modifications. NATURE COMMUNICATIONS, 12 (1). 4011-.

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Abstract

Recent studies suggest that epi-transcriptome regulation via post-transcriptional RNA modifications is vital for all RNA types. Precise identification of RNA modification sites is essential for understanding the functions and regulatory mechanisms of RNAs. Here, we present MultiRM, a method for the integrated prediction and interpretation of post-transcriptional RNA modifications from RNA sequences. Built upon an attention-based multi-label deep learning framework, MultiRM not only simultaneously predicts the putative sites of twelve widely occurring transcriptome modifications (m<sup>6</sup>A, m<sup>1</sup>A, m<sup>5</sup>C, m<sup>5</sup>U, m<sup>6</sup>Am, m<sup>7</sup>G, Ψ, I, Am, Cm, Gm, and Um), but also returns the key sequence contents that contribute most to the positive predictions. Importantly, our model revealed a strong association among different types of RNA modifications from the perspective of their associated sequence contexts. Our work provides a solution for detecting multiple RNA modifications, enabling an integrated analysis of these RNA modifications, and gaining a better understanding of sequence-based RNA modification mechanisms.

Item Type: Article
Uncontrolled Keywords: Humans, RNA, Computational Biology, DNA Methylation, RNA Processing, Post-Transcriptional, Base Sequence, Neural Networks, Computer
Divisions: Faculty of Health and Life Sciences
Faculty of Health and Life Sciences > Institute of Life Courses and Medical Sciences
Faculty of Health and Life Sciences > Institute of Systems, Molecular and Integrative Biology
Faculty of Science and Engineering > School of Electrical Engineering, Electronics and Computer Science
Depositing User: Symplectic Admin
Date Deposited: 12 Jul 2021 09:51
Last Modified: 18 Jan 2023 21:36
DOI: 10.1038/s41467-021-24313-3
Open Access URL: https://www.nature.com/articles/s41467-021-24313-3
Related URLs:
URI: https://livrepository.liverpool.ac.uk/id/eprint/3129728