Statistical modeling of single-cell epitranscriptomics enabled trajectory and regulatory inference of RNA methylation.

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-

SUBMITTER: Wang H 

PROVIDER: S-EPMC11770222 | biostudies-literature | 2025 Jan

REPOSITORIES: biostudies-literature