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Related Experiment Video

Updated: Jun 14, 2025

Nanopore DNA Sequencing for Metagenomic Soil Analysis

Nanopore DNA Sequencing for Metagenomic Soil Analysis

Published on: December 14, 2017

ReadCurrent: a VDCNN-based tool for fast and accurate nanopore selective sequencing.

Kechen Fan^1,2, Mengfan Li^1,3, Jiarong Zhang^1,4

¹Advanced & Interdisciplinary Biotechnology, Academy of Military Medical Sciences, No. 27 Taiping Road, Haidian District, Beijing 100850, China.

Briefings in Bioinformatics

|September 3, 2024

Summary

ReadCurrent, a deep learning tool, enhances nanopore selective sequencing by accurately classifying DNA using electric currents. This computational method offers a faster and more precise alternative to traditional techniques.

Keywords:

VDCNN classification deep learning nanopore sequencing selective sequencing

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Last Updated: Jun 14, 2025

Nanopore DNA Sequencing for Metagenomic Soil Analysis

Nanopore DNA Sequencing for Metagenomic Soil Analysis

Published on: December 14, 2017

Sequencing of mRNA from Whole Blood using Nanopore Sequencing

Sequencing of mRNA from Whole Blood using Nanopore Sequencing

Published on: June 3, 2019

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

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

Published on: March 11, 2020

Area of Science:

Genomics
Bioinformatics
Computational Biology

Background:

Nanopore selective sequencing aims to target specific DNA regions computationally.
Deep learning (DL) models offer speed advantages over sequence alignment but suffer from low accuracy.
Existing DL tools for nanopore sequencing require improvement in classification accuracy.

Purpose of the Study:

To introduce ReadCurrent, a novel DL-based tool for accurate nanopore selective sequencing.
To improve the speed and accuracy of targeted DNA sequencing using nanopore technology.
To provide a computational alternative to experimental enrichment methods.

Main Methods:

ReadCurrent utilizes electric current signals as input for DNA classification.
A modified very deep convolutional neural network (VDCNN) architecture is employed for efficiency.
The tool was evaluated on 10 diverse nanopore sequencing datasets (human, yeast, bacteria, viruses).

Main Results:

ReadCurrent achieved a mean classification accuracy of 98.57%, surpassing other DL methods.
Experimental validation showed an enrichment ratio of 2.85 for microbial DNA from human DNA.
The modified VDCNN architecture resulted in lower computational costs and quicker inference.

Conclusions:

ReadCurrent offers a highly accurate and rapid computational approach for nanopore selective sequencing.
The tool provides a valuable alternative to experimental methods for targeted DNA enrichment.
ReadCurrent demonstrates significant improvements in accuracy and efficiency for nanopore sequencing applications.