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Related Concept Videos

RNA-seq03:21

RNA-seq

RNA sequencing, or RNA-Seq, is a high-throughput sequencing technology used to study the transcriptome of a cell. Transcriptomics helps to interpret the functional elements of a genome and identify the molecular constituents of an organism. Additionally, it also helps in understanding the development of an organism and the occurrence of diseases. 
Before the discovery of RNA-seq, microarray-based methods and Sanger sequencing were used for transcriptome analysis. However, while microarray-based...

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Validating Whole Genome Nanopore Sequencing, using Usutu Virus as an Example
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ModiCal: A Targeted Calibration Workflow for Site-Specific m5C Validation by Nanopore Direct RNA Sequencing.

Zeynep Özrendeci1, Stefan Mündnich1, Stefan Pastore1

  • 1Institute of Pharmaceutical and Biomedical Science (IPBS), Johannes Gutenberg University, 55128 Mainz, Germany.

ACS Chemical Biology
|May 19, 2026
PubMed
Summary
This summary is machine-generated.

This study presents a new method for accurately identifying RNA 5-methylcytidine (m5C) modifications using nanopore direct RNA sequencing. The refined tool, ModiDeC, achieves high site-specific accuracy by combining biochemical validation with iterative neural network refinement.

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Area of Science:

  • Molecular Biology
  • Bioinformatics
  • Genomics

Background:

  • Accurate identification of RNA 5-methylcytidine (m5C) at single-nucleotide resolution is challenging for nanopore direct RNA sequencing (DRS).
  • Existing methods often have high false-positive rates and lack site-specific accuracy for transcriptome-wide profiling.

Purpose of the Study:

  • To repurpose the ModiDeC classifier into a targeted, high-precision validation tool for RNA modification sites.
  • To establish a reproducible and transferable framework for reliable site-specific m5C confirmation using DRS.

Main Methods:

  • A three-step calibration workflow integrating biochemical and computational modules, using synthetic RNAs and yeast models.
  • Iterative retraining of the neural network with unmodified RNA signals to eliminate false positives.
  • Application to human prerRNA and dengue virus genomic RNA for validation across diverse contexts.

Main Results:

  • The refined ModiDeC accurately detected known m5C sites and eliminated off-target predictions.
  • The method successfully identified previously undetected m5C sites and resolved distinct deposition regimes.
  • The calibration logic demonstrated transferability across different RNA types.

Conclusions:

  • A robust framework combining biochemical validation and neural network refinement enables reliable site-specific m5C detection via DRS.
  • This approach significantly improves the accuracy and precision of RNA modification analysis.
  • The developed method is transferable and applicable to various RNA contexts for future research.