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

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      PLoS ONE
      Public Library of Science

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          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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          Bidirectional recurrent neural networks

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            The Vanishing Gradient Problem During Learning Recurrent Neural Nets and Problem Solutions

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              On the limited memory BFGS method for large scale optimization

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                Author and article information

                Contributors
                Role: Editor
                Journal
                PLoS One
                PLoS ONE
                plos
                plosone
                PLoS ONE
                Public Library of Science (San Francisco, CA USA )
                1932-6203
                2017
                5 June 2017
                : 12
                : 6
                : e0178751
                Affiliations
                [001]Faculty of Mathematics, Physics and Informatics, Comenius University in Bratislava, Slovakia
                University of Texas Health Science Center at Houston, UNITED STATES
                Author notes

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

                • Conceptualization: VB TV BB.

                • Data curation: VB.

                • Formal analysis: VB.

                • Funding acquisition: TV BB.

                • Investigation: VB TV BB.

                • Methodology: VB TV BB.

                • Project administration: TV.

                • Resources: TV BB.

                • Software: VB.

                • Supervision: TV.

                • Validation: VB.

                • Visualization: VB.

                • Writing – original draft: VB TV BB.

                • Writing – review & editing: VB TV BB.

                Author information
                http://orcid.org/0000-0003-3898-3447
                Article
                PONE-D-16-48826
                10.1371/journal.pone.0178751
                5459436
                28582401
                34319e1b-2e6b-438f-aa9d-8ec26660f1a8
                © 2017 Boža et al

                This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

                History
                : 10 December 2016
                : 18 May 2017
                Page count
                Figures: 4, Tables: 3, Pages: 13
                Funding
                Funded by: funder-id http://dx.doi.org/10.13039/501100003194, Agentúra Ministerstva školstva, vedy, výskumu a športu SR;
                Award ID: 1/0684/16
                Award Recipient :
                Funded by: funder-id http://dx.doi.org/10.13039/501100003194, Agentúra Ministerstva školstva, vedy, výskumu a športu SR;
                Award ID: 1/0719/14
                Award Recipient :
                Funded by: funder-id http://dx.doi.org/10.13039/501100005357, Agentúra na Podporu Výskumu a Vývoja;
                Award ID: APVV-14-0253
                Award Recipient :
                This research was funded by VEGA grants 1/0684/16 (BB) and 1/0719/14 (TV), and a grant from the Slovak Research and Development Agency APVV-14-0253.
                Categories
                Research Article
                Computer and Information Sciences
                Neural Networks
                Biology and Life Sciences
                Neuroscience
                Neural Networks
                Research and Analysis Methods
                Database and Informatics Methods
                Bioinformatics
                Sequence Analysis
                Sequence Alignment
                Physical sciences
                Mathematics
                Probability theory
                Markov models
                Hidden Markov models
                Research and Analysis Methods
                Database and Informatics Methods
                Bioinformatics
                Sequence Analysis
                Heuristic Alignment Procedure
                Biology and Life Sciences
                Organisms
                Bacteria
                Klebsiella
                Klebsiella Pneumoniae
                Biology and Life Sciences
                Microbiology
                Medical Microbiology
                Microbial Pathogens
                Bacterial Pathogens
                Klebsiella
                Klebsiella Pneumoniae
                Medicine and Health Sciences
                Pathology and Laboratory Medicine
                Pathogens
                Microbial Pathogens
                Bacterial Pathogens
                Klebsiella
                Klebsiella Pneumoniae
                Computer and Information Sciences
                Computer Software
                Open Source Software
                Science Policy
                Open Science
                Open Source Software
                Physical Sciences
                Mathematics
                Probability Theory
                Probability Distribution
                Biology and Life Sciences
                Molecular Biology
                Molecular Biology Techniques
                Sequencing Techniques
                High Throughput Sequencing
                Research and Analysis Methods
                Molecular Biology Techniques
                Sequencing Techniques
                High Throughput Sequencing
                Custom metadata
                Data used in this study are available from EMBL-EBI European Nucleotide Archive (accession numbers ERR1147230 and SAMEA3713789) and from the Loman lab website at the University of Birmingham at http://s3.climb.ac.uk/nanopore/R9_Ecoli_K12_MG1655_lambda_MinKNOW_0.51.1.62.tar.

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