Extracting candidate motif patterns for multiple RNA modifications using direct RNA sequencing

RNA modifications have emerged as key regulators of virtually all aspects of RNA biology, driving growing interest in computational approaches for their transcriptome-wide detection. For some RNA modifications, the search space can be reduced by exploiting characteristic sequence motifs, as exemplified by the canonical DRACH motif associated with m⁶A. However, comparable sequence preferences remain largely unknown for most other RNA modifications, making systematic motif discovery considerably more challenging. Here, we applied exponential population analysis to direct RNA sequencing data from HEK293T cells and peripheral human blood to identify candidate motif patterns for pseudouridine (Ψ), inosine (I), and 5-methylcytosine (m⁵C). Our results demonstrate that exponential population analysis provides a systematic framework for identifying candidate motif patterns from nanopore-derived RNA modification predictions, thereby prioritizing sequence contexts for future biological validation.