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. 2022 Apr 20;23(1):142.
doi: 10.1186/s12859-022-04686-y.

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

Don Neumann  1 , Anireddy S N Reddy  2 , Asa Ben-Hur  3
Affiliations

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

Don Neumann et al. BMC Bioinformatics. .

Abstract

Background: Despite recent progress in basecalling of Oxford nanopore DNA sequencing data, its wide adoption is still being hampered by its relatively low accuracy compared to short read technologies. Furthermore, very little of the recent research was focused on basecalling of RNA data, which has different characteristics than its DNA counterpart.

Results: We fill this gap by benchmarking a fully convolutional deep learning basecalling architecture with improved performance compared to Oxford nanopore's RNA basecallers.

Availability: The source code for our basecaller is available at: https://github.com/biodlab/RODAN .

Keywords: Convolutional networks; Long read sequencing; Oxford nanopore; RNA basecalling.

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Conflict of interest statement

The authors declare that they have no competing interests.

Figures

Fig. 1
Fig. 1
The RODAN architecture. The normalized signal is passed through a succession of convolutional blocks which gradually incorporate surrounding information. Each block is composed of several processing steps (convolution, activation, batch normalization etc.), which are standard building blocks in the construction of deep neural networks. The final output is passed through a fully connected layer to produce the decoded sequence of nucleotides
Fig. 2
Fig. 2
Read statistics. For each of the five datasets we show histograms of read length in (a), and basecalling calling accuracy as a function of read length
See this image and copyright information in PMC

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