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. 2017 Jun 5;12(6):e0178751.
doi: 10.1371/journal.pone.0178751. eCollection 2017.

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

Vladimír Boža  1 , Broňa Brejová  1 , Tomáš Vinař  1
Affiliations

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

Vladimír Boža et al. PLoS One. .

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.

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Conflict of interest statement

Competing Interests: The authors have declared that no competing interests exist.

Figures

Fig 1
Fig 1. Raw signal from MinION and its segmentation to events.
The plot was generated from the E. coli data (http://www.ebi.ac.uk/ena/data/view/ERR1147230).
Fig 2
Fig 2. Schematics of a bidirectional recurrent neural network.
Fig 3
Fig 3. DeepNano reduces bias in 6-mer composition.
Comparison of 6-mer content in Klebsiella reference genome and base-called reads by Metrichor (left) and DeepNano (right). From top to bottom: template, complement, 2D.
Fig 4
Fig 4. Abudances for repetitive 6-mers.
See this image and copyright information in PMC

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