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Link to original content: https://doi.org/10.1038/nbt.2280
Hybrid error correction and de novo assembly of single-molecule sequencing reads | Nature Biotechnology
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Hybrid error correction and de novo assembly of single-molecule sequencing reads

Nature Biotechnology volume 30, pages 693–700 (2012)Cite this article

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Abstract

Single-molecule sequencing instruments can generate multikilobase sequences with the potential to greatly improve genome and transcriptome assembly. However, the error rates of single-molecule reads are high, which has limited their use thus far to resequencing bacteria. To address this limitation, we introduce a correction algorithm and assembly strategy that uses short, high-fidelity sequences to correct the error in single-molecule sequences. We demonstrate the utility of this approach on reads generated by a PacBio RS instrument from phage, prokaryotic and eukaryotic whole genomes, including the previously unsequenced genome of the parrot Melopsittacus undulatus, as well as for RNA-Seq reads of the corn (Zea mays) transcriptome. Our long-read correction achieves >99.9% base-call accuracy, leading to substantially better assemblies than current sequencing strategies: in the best example, the median contig size was quintupled relative to high-coverage, second-generation assemblies. Greater gains are predicted if read lengths continue to increase, including the prospect of single-contig bacterial chromosome assembly.

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Figure 1: The PBcR single-molecule read correction and assembly method.
Figure 2: Long-reads yield assembly improvements, even at low coverage.
Figure 3: Contig sizes for various combinations of sequencing technologies.
Figure 4: Error correction of RNA-Seq data provides more accurate mapping of transcripts.

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Acknowledgements

We thank Pacific Biosciences, Roche 454, Illumina, BGI and the Duke Genome Center for the generation and/or release of many of the data sets examined herein, and to the Assemblathon working group for the coordination and release of the parrot genome data. This publication was developed and funded in part under Agreement No. HSHQDC-07-C-00020 awarded by the US Department of Homeland Security for the management and operation of the National Biodefense Analysis and Countermeasures Center (NBACC), a Federally Funded Research and Development Center. The views and conclusions contained in this document are those of the authors and should not be interpreted as necessarily representing the official policies, either expressed or implied, of the US Department of Homeland Security. The Department of Homeland Security does not endorse any products or commercial services mentioned in this publication. The work conducted by the US Department of Energy Joint Genome Institute is supported by the Office of Science of the US Department of Energy under Contract No. DE-AC02-05CH11231. This work was also funded in part by the US National Institutes of Health (NIH) R01-HG006677-12 (M.C.S.), NIH 2R01GM077117-04A1 (B.P.W.), the state of Maryland (D.A.R.), National Science Foundation IOS-1032105 to W.R.M., and Howard Hughes Medical Institute and NIH Directors Pioneer Award to E.D.J.

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Authors and Affiliations

  1. National Biodefense Analysis and Countermeasures Center, Frederick, Maryland, USA

    Sergey Koren & Adam M Phillippy

  2. Center for Bioinformatics and Computational Biology, University of Maryland, College Park, Maryland, USA

    Sergey Koren

  3. Cold Spring Harbor Laboratory, Cold Spring Harbor, New York, USA

    Michael C Schatz & W Richard McCombie

  4. The J. Craig Venter Institute, Rockville, Maryland, USA.,

    Brian P Walenz

  5. DOE Joint Genome Institute, Walnut Creek, California, USA

    Jeffrey Martin & Zhong Wang

  6. Department of Neurobiology, Howard Hughes Medical Institute, Duke University Medical Center, Durham, North Carolina, USA.,

    Jason T Howard, Ganeshkumar Ganapathy & Erich D Jarvis

  7. Department of Microbiology & Immunology, Institute for Genome Sciences, University of Maryland School of Medicine, Baltimore, Maryland, USA

    David A Rasko

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Contributions

S.K. and A.M.P. conceived and designed the algorithm. S.K. implemented the algorithm and carried out the de novo assembly experiments. S.K., M.C.S. and A.M.P. drafted the manuscript, ran experiments and contributed analysis. B.P.W. modified the Celera Assembler to support long sequencing reads and developed the BOGART unitigger. J.M. and Z.W. sequenced Z. mays cDNA and performed analysis. J.H., G.G. and E.D.J. sequenced M. undulatus and performed analysis of vocal learning genes. D.A.R. provided and sequenced E. coli strains. W.R.M. sequenced S. cerevisiae S228c. All authors read and approved the final manuscript.

Corresponding authors

Correspondence to Sergey Koren or Adam M Phillippy.

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Competing interests

W.R.M. has participated in Illumina-sponsored meetings over the past four years and received travel reimbursement and honoraria for presenting at these events.

Supplementary information

Supplementary Text and Figures

Supplementary Tables 1-7, Supplementary Notes 1–6 and Supplementary Figures 1–16 (PDF 2414 kb)

Supplementary Dataset 1

Celera Assembler and AMOS source code utilized for PacBio correction experiments described in this publication. (ZIP 9510 kb)

Supplementary Dataset 2

Celera Assembler source code utilized for PacBio assembly experiments described in this publication. (ZIP 3700 kb)

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Koren, S., Schatz, M., Walenz, B. et al. Hybrid error correction and de novo assembly of single-molecule sequencing reads. Nat Biotechnol 30, 693–700 (2012). https://doi.org/10.1038/nbt.2280

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