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A complete bacterial genome assembled de novo using only nanopore sequencing data

Nicholas J. Loman, Joshua Quick, Jared T. Simpson
doi: https://doi.org/10.1101/015552
Nicholas J. Loman
1 Institute of Microbiology and Infection, University of Birmingham, Birmingham, B15 2TT, UK
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Joshua Quick
1 Institute of Microbiology and Infection, University of Birmingham, Birmingham, B15 2TT, UK
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Jared T. Simpson
2 Ontario Institute for Cancer Research, Toronto, Ontario, Canada
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Abstract

A method for de novo assembly of data from the Oxford Nanopore MinION instrument is presented which is able to reconstruct the sequence of an entire bacterial chromosome in a single contig. Initially, overlaps between nanopore reads are detected. Reads are then subjected to one or more rounds of error correction by a multiple alignment process employing partial order graphs. After correction, reads are assembled using the Celera assembler. Finally, the assembly is polished using signal-level data from the nanopore employing a novel hidden Markov model. We show that this method is able to assemble nanopore reads from Escherichia coli K-12 MG1655 into a single contig of length 4.6Mb permitting a full reconstruction of gene order. The resulting draft assembly has 98.4% nucleotide identity compared to the finished reference genome. After polishing the assembly with our signal-level HMM, the nucleotide identity is improved to 99.4%. We show that MinION sequencing data can be used to reconstruct genomes without the need for a reference sequence or data from other sequencing platforms.

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The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY 4.0 International license.
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Posted March 11, 2015.
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A complete bacterial genome assembled de novo using only nanopore sequencing data
Nicholas J. Loman, Joshua Quick, Jared T. Simpson
bioRxiv 015552; doi: https://doi.org/10.1101/015552
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A complete bacterial genome assembled de novo using only nanopore sequencing data
Nicholas J. Loman, Joshua Quick, Jared T. Simpson
bioRxiv 015552; doi: https://doi.org/10.1101/015552

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