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

Zitao Song, Daiyun Huang (), Bowen Song, Kunqi Chen, Yiyou Song, Gang Liu, Jionglong Su, João Pedro de Magalhães, Daniel J. Rigden and Jia Meng ()
Additional contact information
Zitao Song: Xi’an Jiaotong-Liverpool University
Daiyun Huang: Xi’an Jiaotong-Liverpool University
Bowen Song: Xi’an Jiaotong-Liverpool University
Kunqi Chen: Laboratory of Ministry of Education for Gastrointestinal Cancer, School of Basic Medical Sciences, Fujian Medical University
Yiyou Song: Xi’an Jiaotong-Liverpool University
Gang Liu: Xi’an Jiaotong-Liverpool University
Jionglong Su: School of AI and Advanced Computing, XJTLU Entrepreneur College (Taicang), Xi’an Jiaotong-Liverpool University
João Pedro de Magalhães: Institute of Ageing and Chronic Disease, University of Liverpool
Daniel J. Rigden: Institute of Systems, Molecular and Integrative Biology, University of Liverpool
Jia Meng: Xi’an Jiaotong-Liverpool University

Nature Communications, 2021, vol. 12, issue 1, 1-11

Abstract: 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 (m6A, m1A, m5C, m5U, m6Am, m7G, 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.

Date: 2021
References: Add references at CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
https://www.nature.com/articles/s41467-021-24313-3 Abstract (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:nat:natcom:v:12:y:2021:i:1:d:10.1038_s41467-021-24313-3

Ordering information: This journal article can be ordered from
https://www.nature.com/ncomms/

DOI: 10.1038/s41467-021-24313-3

Access Statistics for this article

Nature Communications is currently edited by Nathalie Le Bot, Enda Bergin and Fiona Gillespie

More articles in Nature Communications from Nature
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().