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, Jia Meng

Research output: Contribution to journalArticlepeer-review

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.
Original languageEnglish
Article number4011
Number of pages11
JournalNature Communications
Volume12
Issue number1
Early online date29 Jun 2021
DOIs
Publication statusPublished - 1 Dec 2021

Fingerprint

Dive into the research topics of 'Attention-based multi-label neural networks for integrated prediction and interpretation of twelve widely occurring RNA modifications'. Together they form a unique fingerprint.

Cite this