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

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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Cited by papers we hold 11

• Nanopore Technology and Its Applications in Gene Sequencing · 2021
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• Opportunities and challenges in long-read sequencing data analysis · 2020
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• Optimized CRISPR guide RNA design for two high-fidelity Cas9 variants by deep learning · 2019
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• Performance of neural network basecalling tools for Oxford Nanopore sequencing · 2019
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• Artificial intelligence for precision medicine in neurodevelopmental disorders · 2019
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• Evaluation of Oxford Nanopore’s MinION Sequencing Device for Microbial Whole Genome Sequencing Applications · 2018
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• From squiggle to basepair: computational approaches for improving nanopore sequencing read accuracy · 2018
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• Chiron: translating nanopore raw signal directly into nucleotide sequence using deep learning · 2018
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• Opportunities and obstacles for deep learning in biology and medicine · 2018
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• Deepbinner: Demultiplexing barcoded Oxford Nanopore reads with deep convolutional neural networks · 2018
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• The Oxford Nanopore MinION: delivery of nanopore sequencing to the genomics community · 2016
  Corrected

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