Geographic encoding of transcripts enabled high-accuracy and isoform-aware deep learning of RNA methylation

Huang, Daiyun, Chen, Kunqi, Song, Bowen, Wei, Zhen, Su, Jionglong, Coenen, Frans ORCID: 0000-0003-1026-6649, 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 (2022) Geographic encoding of transcripts enabled high-accuracy and isoform-aware deep learning of RNA methylation. NUCLEIC ACIDS RESEARCH, 50 (18). pp. 10290-10310.

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Official URL: http://dx.doi.org/10.1093/nar/gkac830

Abstract

As the most pervasive epigenetic mark present on mRNA and lncRNA, N6-methyladenosine (m6A) RNA methylation regulates all stages of RNA life in various biological processes and disease mechanisms. Computational methods for deciphering RNA modification have achieved great success in recent years; nevertheless, their potential remains underexploited. One reason for this is that existing models usually consider only the sequence of transcripts, ignoring the various regions (or geography) of transcripts such as 3'UTR and intron, where the epigenetic mark forms and functions. Here, we developed three simple yet powerful encoding schemes for transcripts to capture the submolecular geographic information of RNA, which is largely independent from sequences. We show that m6A prediction models based on geographic information alone can achieve comparable performances to classic sequence-based methods. Importantly, geographic information substantially enhances the accuracy of sequence-based models, enables isoform- and tissue-specific prediction of m6A sites, and improves m6A signal detection from direct RNA sequencing data. The geographic encoding schemes we developed have exhibited strong interpretability, and are applicable to not only m6A but also N1-methyladenosine (m1A), and can serve as a general and effective complement to the widely used sequence encoding schemes in deep learning applications concerning RNA transcripts.

Item Type:	Article
Uncontrolled Keywords:	Protein Isoforms, RNA, RNA, Messenger, 3' Untranslated Regions, Methylation, RNA, Long Noncoding, Deep Learning
Divisions:	Faculty of Health and Life Sciences Faculty of Science and Engineering > School of Electrical Engineering, Electronics and Computer Science 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
Depositing User:	Symplectic Admin
Date Deposited:	07 Oct 2022 10:17
Last Modified:	18 Jan 2023 20:41
DOI:	10.1093/nar/gkac830
Open Access URL:	https://academic.oup.com/nar/advance-article/doi/1...
Related URLs:	Author Publisher
URI:	https://livrepository.liverpool.ac.uk/id/eprint/3165106