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Unsupervised Reference Modeling of Nanopore Signals for DNA/RNA Modification Detection.

Yongji Zou^1,2, Mian Umair Ahsan¹, Kai Wang^1,3

¹Center for Cellular and Molecular Therapeutics, Children's Hospital of Philadelphia, Philadelphia, PA 19104, USA.

|May 27, 2026

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This study introduces an unsupervised framework for DNA and RNA modification detection using nanopore sequencing. While effective on synthetic data, its performance on complex biological samples highlights challenges in real-world application.

Area of Science:

Genomics
Bioinformatics
Molecular Biology

Background:

Nanopore sequencing detects DNA and RNA chemical modifications via ionic current signals.
Accurate modification detection is hindered by limited labeled data and experimental variability.

Purpose of the Study:

To develop a scalable unsupervised framework for de novo DNA and RNA modification discovery.
To address challenges in modification detection using nanopore sequencing data.

Main Methods:

A CNN-Transformer variational autoencoder (VAE) learns reference signal distributions from unmodified sequences.
Large-scale data training utilizes streaming sampling and k-mer-aware soft balancing.
Modification evidence is generated by scoring nucleotides with VAE reconstruction error and aggregating read-level signals.

Keywords:

anomaly detection base modification detection nanopore sequencing unsupervised learning variational autoencoder

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Main Results:

Models trained on unmodified sequences show strong discrimination on synthetic modified oligonucleotides.
Performance degrades on cell line samples due to biological noise and heterogeneity.
Site-level anomaly scores reveal patterns corresponding to known modification-enriched regions despite reduced classification accuracy.

Conclusions:

Large-scale unsupervised reference modeling is feasible for de novo modification detection.
Translating models from synthetic data to robust genome-wide detection in biological samples presents significant challenges.