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RODAN: a fully convolutional architecture for basecalling nanopore RNA sequencing data.

Don Neumann¹, Anireddy S N Reddy², Asa Ben-Hur³

¹Department of Computer Science, Colorado State University, 1873 Campus Delivery, Fort Collins, CO, 80523-1873, USA.

BMC Bioinformatics

|April 21, 2022

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We developed a deep learning model for RNA sequencing basecalling, improving accuracy over existing Oxford Nanopore technologies. This advancement aims to enhance the adoption of nanopore sequencing for RNA analysis.

Area of Science:

Bioinformatics
Computational Biology
Genomics

Background:

Oxford Nanopore sequencing offers long reads but faces accuracy challenges compared to short-read technologies.
Limited research exists on basecalling for RNA sequencing data, which differs significantly from DNA data.

Purpose of the Study:

To address the gap in RNA sequencing basecalling by evaluating a novel deep learning architecture.
To improve the accuracy of Oxford Nanopore RNA basecalling.

Main Methods:

Benchmarking a fully convolutional deep learning architecture for RNA basecalling.
Comparison against existing Oxford Nanopore RNA basecalling tools.

Main Results:

The developed deep learning basecaller demonstrates improved performance over current Oxford Nanopore RNA basecallers.

Keywords:

Convolutional networks Long read sequencing Oxford nanopore RNA basecalling

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Last Updated: Sep 26, 2025

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Use of the Ramsay Assay to Measure Fluid Secretion and Ion Flux Rates in the Drosophila melanogaster Malpighian Tubule

Published on: November 25, 2015

The proposed architecture effectively handles the unique characteristics of RNA sequencing data.

Conclusions:

The novel deep learning approach offers a significant improvement for Oxford Nanopore RNA sequencing basecalling.
This work facilitates wider adoption of nanopore technology for RNA analysis.