Wang, Haozhe, Wang, Yue, Zhou, Jingxian, Song, Bowen, Tu, Gang, Nguyen, Anh 
ORCID: 0000-0002-1449-211X, Su, Jionglong, Coenen, Frans ORCID: 0000-0003-1026-6649, Wei, Zhi, Rigden, Daniel J ORCID: 0000-0002-7565-8937 et al (show 1 more authors)
(2025)
Statistical modeling of single-cell epitranscriptomics enabled trajectory and regulatory inference of RNA methylation.
Cell genomics, 5 (1).
100702-.
ISSN 2666-979X, 2666-979X
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Text
1-s2.0-S2666979X24003318-main.pdf - Open Access published version Available under License Creative Commons Attribution Non-commercial No Derivatives. Download (2MB) | Preview |
Abstract
As a fundamental mechanism for gene expression regulation, post-transcriptional RNA methylation plays versatile roles in various biological processes and disease mechanisms. Recent advances in single-cell technology have enabled simultaneous profiling of transcriptome-wide RNA methylation in thousands of cells, holding the promise to provide deeper insights into the dynamics, functions, and regulation of RNA methylation. However, it remains a major challenge to determine how to best analyze single-cell epitranscriptomics data. In this study, we developed SigRM, a computational framework for effectively mining single-cell epitranscriptomics datasets with a large cell number, such as those produced by the scDART-seq technique from the SMART-seq2 platform. SigRM not only outperforms state-of-the-art models in RNA methylation site detection on both simulated and real datasets but also provides rigorous quantification metrics of RNA methylation levels. This facilitates various downstream analyses, including trajectory inference and regulatory network reconstruction concerning the dynamics of RNA methylation.
| Item Type: | Article |
|---|---|
| Uncontrolled Keywords: | Humans, RNA, Models, Statistical, Sequence Analysis, RNA, Epigenesis, Genetic, Methylation, Single-Cell Analysis, Transcriptome, RNA Methylation |
| Divisions: | Faculty of Health and Life Sciences Faculty of Science and Engineering Faculty of Science and Engineering > School of Electrical Engineering, Electronics and Computer Science Faculty of Health and Life Sciences > Institute of Systems, Molecular and Integrative Biology |
| Depositing User: | Symplectic Admin |
| Date Deposited: | 09 Dec 2024 08:24 |
| Last Modified: | 21 Jan 2025 12:07 |
| DOI: | 10.1016/j.xgen.2024.100702 |
| Related Websites: | |
| URI: | https://livrepository.liverpool.ac.uk/id/eprint/3189062 |
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