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ModiDeC: a multi-RNA modification classifier for direct nanopore sequencing.

Nicolò Alagna¹, Stefan Mündnich², Johannes Miedema¹

¹Institute of Human Genetics, University Medical Center Mainz, Mainz 55128, Germany.

Nucleic Acids Research

|July 19, 2025

Summary

This summary is machine-generated.

ModiDeC, a new deep-learning tool, accurately identifies multiple RNA modifications using direct RNA sequencing. This advance aids epitranscriptome analysis in various biological samples.

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Area of Science:

Molecular Biology
Bioinformatics
Genomics

Background:

RNA modifications are vital for cellular functions.
Accurate detection of RNA modifications is crucial for understanding gene regulation.
Existing methods may have limitations in identifying multiple modification types simultaneously.

Purpose of the Study:

To develop a deep-learning classifier, ModiDeC, for identifying and distinguishing multiple RNA modifications.
To create an extensive database of RNA sequences for training and validation.
To provide a user-friendly tool for epitranscriptome analysis.

Main Methods:

Development of a deep-learning model (ModiDeC) for RNA modification classification.
Generation of in vitro-transcribed and synthetic RNA sequences using RNA004 and RNA002 chemistries.
Validation of ModiDeC using synthetic data, HEK293T cells, and human blood samples.

Main Results:

ModiDeC accurately identifies and distinguishes five types of RNA modifications: N6-methyladenosine, inosine, pseudouridine, 2'-O-methylguanosine, and N1-methyladenosine.
High accuracy was observed across different sequence motifs and in various biological samples.
The tool demonstrated reproducibility and a low false-positive rate.

Conclusions:

ModiDeC is a powerful and adaptable tool for analyzing the epitranscriptome.
The graphical user interface and Epi2ME pipeline facilitate customization for specific research needs.
ModiDeC advances the field of RNA modification analysis.