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Next-generation Sequencing

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Updated: Jun 15, 2025

Rare Event Detection Using Error-corrected DNA and RNA Sequencing

Rare Event Detection Using Error-corrected DNA and RNA Sequencing

Published on: August 3, 2018

Adapting nanopore sequencing basecalling models for modification detection via incremental learning and anomaly

Ziyuan Wang¹, Yinshan Fang², Ziyang Liu^1,3

¹Department of Pharmacy Practice and Science, University of Arizona, Tucson, AZ, USA.

Nature Communications

|August 21, 2024

Summary

This study introduces a machine learning pipeline for detecting nucleotide modifications using nanopore sequencing. The method accurately identifies modifications at single-molecule and single-nucleotide resolution, even in complex biological samples.

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Last Updated: Jun 15, 2025

Rare Event Detection Using Error-corrected DNA and RNA Sequencing

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Published on: August 3, 2018

Validating Whole Genome Nanopore Sequencing, using Usutu Virus as an Example

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Detection of Copy Number Alterations Using Single Cell Sequencing

Detection of Copy Number Alterations Using Single Cell Sequencing

Published on: February 17, 2017

Area of Science:

Genomics
Bioinformatics
Molecular Biology

Background:

Nanopore sequencing offers real-time, long-read sequencing but requires accurate basecalling, especially for modified nucleotides.
Detecting nucleotide modifications is crucial for understanding gene regulation and cellular processes.

Purpose of the Study:

To develop a machine learning pipeline for accurate, single-molecule, single-nucleotide detection of nucleotide modifications using nanopore sequencing data.
To adapt existing basecalling algorithms for enhanced modification detection.

Main Methods:

Utilized incremental learning (IL) to improve basecalling of modification-rich sequences.
Applied anomaly detection (AD) on individual nucleotides to identify modification status.
Developed a pipeline for sequence context-free modification detection.

Main Results:

Successfully benchmarked the pipeline using control oligos.
Applied the workflow to yeast tRNAs, E.coli genomic DNA, and human mRNA.
Demonstrated cross-species detection of N6-methyladenosine (m6A) and simultaneous detection of N1-methyladenosine (m1A) and m6A.

Conclusions:

The IL-AD pipeline enables precise, context-free detection of nucleotide modifications at single-molecule resolution.
This approach advances the capabilities of nanopore sequencing for epigenetic and epitranscriptomic studies.