ArticleNucleic acids research2025
ModiDeC: a multi-RNA modification classifier for direct nanopore sequencing.
Article in Nucleic acids research, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 citing papers in PubMed.
- Quantifying replication fidelity of unnatural base pairs using nanopore sequencing.Nature communications · 2026Article
- ModiCal: A Targeted Calibration Workflow for Site-Specific mACS chemical biology · 2026Article
- Nanopore Sequencing Reveals rRNA Modification Changes in Human Cells Experiencing Oxidative or Inflammatory Stress.ACS chemical biology · 2026Article
- MethyNano: supervised contrastive pretraining enables robust and generalizable methylation detection from nanopore sequencing.Bioinformatics (Oxford, England) · 2026Article
- Article
- Systematic assessment of diverse RNA modifications using nanopore direct RNA sequencing.Nucleic acids research · 2026Article
- Probing the epitranscriptome and RNA damage with nanopore direct RNA sequencing.RNA (New York, N.Y.) · 2026Review
- Nanopore direct RNA sequencing for RNA modification analysis: workflow assessment and computational tool benchmarking.Advanced biotechnology · 2026Article
- Ab initio detection of multiple epitranscriptomic modifications from Oxford nanopore technology direct RNA sequencing data.Briefings in bioinformatics · 2026Article
- m6AHD: a new framework for identifying abnormal N6-methyladenosine (m6A) in heart diseases based on sequencing features.Frontiers in genetics · 2026Article
- Nanopore sequencing reveals operon-specific ribosome remodeling accompanying naphthyridone resistance inbioRxiv : the preprint server for biology · 2025Article
- Advances in Quantitative Techniques for Mapping RNA Modifications.Life (Basel, Switzerland) · 2025Review
- The RMaP challenge of predicting RNA modifications by nanopore sequencing.Communications chemistry · 2025Article
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Authors and funding
13 authors.
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Abstract
RNA modifications play a crucial role in various cellular functions. Here, we present ModiDeC, a deep-learning-based classifier able to identify and distinguish multiple RNA modifications (N6-methyladenosine, inosine, pseudouridine, 2'-O-methylguanosine, and N1-methyladenosine) using direct RNA sequencing. Alongside ModiDeC, we provide an extensive database of in vitro-transcribed and synthetic sequences generated with both the new RNA004 chemistry and the old RNA002 kit. We show that RNA modifications can be accurately recognized and distinguished across different sequence motifs using synthetic data as well as in HEK293T cells and human blood samples. ModiDeC comes with a graphical user interface and an Epi2ME pipeline that allows easy customization and adaptation to specific research questions, such as learning and classifying additional RNA modifications and further sequence motifs. The reproducibility across samples, together with the low rate of false positives, underscores the potential of ModiDeC as a powerful tool for advancing the analysis of the epitranscriptome and RNA modification.
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