Abstract

Monitoring the progress of DNA molecules through a membrane pore has been postulated as a method for sequencing DNA for several decades. Recently, a nanopore-based sequencing instrument, the Oxford Nanopore MinION, has become available, and we used this for sequencing the Saccharomyces cerevisiae genome. To make use of these data, we developed a novel open-source hybrid error correction algorithm Nanocorr specifically for Oxford Nanopore reads, because existing packages were incapable of assembling the long read lengths (5–50 kbp) at such high error rates (between ∼5% and 40% error). With this new method, we were able to perform a hybrid error correction of the nanopore reads using complementary MiSeq data and produce a de novo assembly that is highly contiguous and accurate: The contig N50 length is more than ten times greater than an Illumina-only assembly (678 kb versus 59.9 kbp) and has >99.88% consensus identity when compared to the reference. Furthermore, the assembly with the long nanopore reads presents a much more complete representation of the features of the genome and correctly assembles gene cassettes, rRNAs, transposable elements, and other genomic features that were almost entirely absent in the Illumina-only assembly.

Keywords

Nanopore sequencingMinionContigNanoporeHybrid genome assemblySequence assemblyBiologyComputational biologyGenomeDNA sequencingTransposable elementGeneticsIllumina dye sequencingError detection and correctionDNAComputer scienceGeneAlgorithmNanotechnology

MeSH Terms

DNA Transposable ElementsDNAFungalEscherichia coliGenomicsNanoporesSaccharomyces cerevisiaeSequence AlignmentSequence AnalysisDNA

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Publication Info

Year
2015
Type
article
Volume
25
Issue
11
Pages
1750-1756
Citations
412
Access
Closed

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412
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22
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314
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Cite This

Sara Goodwin, James Gurtowski, Scott Ethe-Sayers et al. (2015). Oxford Nanopore sequencing, hybrid error correction, and de novo assembly of a eukaryotic genome. Genome Research , 25 (11) , 1750-1756. https://doi.org/10.1101/gr.191395.115

Identifiers

DOI
10.1101/gr.191395.115
PMID
26447147
PMCID
PMC4617970

Data Quality

Data completeness: 90%