RNA modifications act as key regulators of many aspects of RNA biology, which has fueled interest in computational methods for detecting them across the transcriptome. For some modifications, the search space can be narrowed by exploiting characteristic sequence motifs. The best-known example is the canonical DRACH motif associated with N6-methyladenosine, or m6A. However, comparable sequence preferences remain largely unknown for most other RNA modifications, making systematic motif discovery considerably more challenging.

In the new study, Nicolò Alagna, Johannes Miedema, Yahia Basiony and Susanne Gerber applied a technique called exponential population analysis to direct RNA sequencing data. The data came from two human sources: HEK293T cells, a widely used laboratory cell line, and peripheral human blood samples. The goal was to identify candidate motif patterns for three RNA modifications: pseudouridine, inosine and 5-methylcytosine.

Direct RNA sequencing, a nanopore-based approach, allows RNA molecules to be read natively, and computational predictions of modification sites can be derived from such data. The exponential population analysis method provides a systematic way to mine these predictions for recurring sequence contexts, the authors report. Rather than delivering confirmed biological mechanisms, the analysis prioritizes sequence contexts that merit future biological validation.

The authors emphasize that the framework’s output is a set of candidate motif patterns, not experimentally validated motifs. Because sequence preferences for pseudouridine, inosine and 5-methylcytosine are not well characterized, the identified candidates could guide the design of follow-up experiments aimed at confirming whether the predicted contexts reflect genuine modification signatures. The study was published as an open-access research article on 23 October 2026, and the version shared early is subject to further editorial edits before the final version of record appears.

Subject of Research: Computational discovery of candidate sequence motifs for RNA modifications using direct RNA sequencing

Article Title: Extracting candidate motif patterns for multiple RNA modifications using direct RNA sequencing

Article References: Alagna, N., Miedema, J., Basiony, Y., & Gerber, S. (2026). Extracting candidate motif patterns for multiple RNA modifications using direct RNA sequencing. BMC Bioinformatics, 27(1), Article 246. https://doi.org/10.1186/s12859-026-06724-5

Image Credits: AI Generated

DOI: 10.1186/s12859-026-06724-5

Keywords: RNA modifications, direct RNA sequencing, nanopore sequencing, pseudouridine, inosine, 5-methylcytosine, m6A, DRACH motif, exponential population analysis, HEK293T cells, transcriptome, motif discovery