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

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.