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-.
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 |
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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 |