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Related Experiment Video

Updated: Dec 9, 2025

Sequencing of mRNA from Whole Blood using Nanopore Sequencing

Sequencing of mRNA from Whole Blood using Nanopore Sequencing

Published on: June 3, 2019

Molecular barcoding of native RNAs using nanopore sequencing and deep learning.

Martin A Smith^1,2,3,4, Tansel Ersavas¹, James M Ferguson¹

¹Garvan Institute of Medical Research, Darlinghurst 2010, NSW, Australia.

Genome Research

|September 10, 2020

Summary

Direct RNA nanopore sequencing now includes molecular barcoding. Our new DeePlexiCon method uses deep learning to accurately classify and demultiplex native RNA sequencing data, improving cost-effectiveness and enabling low-input sample analysis.

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Nanopore DNA Sequencing for Metagenomic Soil Analysis

Nanopore DNA Sequencing for Metagenomic Soil Analysis

Published on: December 14, 2017

Area of Science:

Molecular Biology
Bioinformatics
Genomics

Background:

Direct RNA nanopore sequencing offers RNA molecule analysis without cDNA conversion.
Limited RNA availability in biological samples hinders current direct RNA sequencing applications due to lack of molecular barcoding.
Multiplexing is crucial for cost-effective analysis of limited biological samples.

Purpose of the Study:

To develop the first experimental protocol and algorithm for barcoding and demultiplexing direct RNA nanopore sequencing data.
To introduce a novel deep learning approach for accurate classification of raw nanopore signal data.
To enhance the applicability of direct RNA sequencing for low-input biological samples.

Main Methods:

Transformation of raw nanopore signal current intensities into image or pixel array data.
Application of a deep learning algorithm for classification of signal-to-image data.
Development of an experimental protocol for barcoding and demultiplexing direct RNA sequencing libraries.

Main Results:

The DeePlexiCon method achieves high accuracy in classifying nanopore reads.
Demonstrated classification of 93% of reads with 95.1% accuracy.
Achieved classification of 60% of reads with 99.9% accuracy, showcasing robust performance.
Successfully developed the first protocol for barcoding and demultiplexing native RNA sequencing libraries.

Conclusions:

The DeePlexiCon method provides an efficient and simple multiplexing strategy for native RNA sequencing.
This approach significantly improves the cost-effectiveness of nanopore sequencing technology.
Facilitates the analysis of precious, low-input biological samples using direct RNA sequencing.
Highlights the potential of signal-to-image conversion and deep learning for nanopore data analysis.