DeepNano: Deep recurrent neural networks for base calling in MinION nanopore reads.

Journal: PloS one
Published Date:

Abstract

The MinION device by Oxford Nanopore produces very long reads (reads over 100 kBp were reported); however it suffers from high sequencing error rate. We present an open-source DNA base caller based on deep recurrent neural networks and show that the accuracy of base calling is much dependent on the underlying software and can be improved by considering modern machine learning methods. By employing carefully crafted recurrent neural networks, our tool significantly improves base calling accuracy on data from R7.3 version of the platform compared to the default base caller supplied by the manufacturer. On R9 version, we achieve results comparable to Nanonet base caller provided by Oxford Nanopore. Availability of an open source tool with high base calling accuracy will be useful for development of new applications of the MinION device, including infectious disease detection and custom target enrichment during sequencing.

Authors

  • Vladimir Boza
    Powerful Medical, Bratislavska 81/37, 931 01 Samorin, Slovakia.
  • Broňa Brejová
    Faculty of Mathematics, Physics and Informatics, Comenius University in Bratislava, Slovakia.
  • Tomáš Vinař
    Faculty of Mathematics, Physics and Informatics, Comenius University in Bratislava, Slovakia.