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De novo sequencing and variant calling with nanopores using PoreSeq

Nature Biotechnology volume 33, pages 1087–1091 (2015)Cite this article

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Abstract

The accuracy of sequencing single DNA molecules with nanopores is continually improving, but de novo genome sequencing and assembly using only nanopore data remain challenging. Here we describe PoreSeq, an algorithm that identifies and corrects errors in nanopore sequencing data and improves the accuracy of de novo genome assembly with increasing coverage depth. The approach relies on modeling the possible sources of uncertainty that occur as DNA transits through the nanopore and finds the sequence that best explains multiple reads of the same region. PoreSeq increases nanopore sequencing read accuracy of M13 bacteriophage DNA from 85% to 99% at 100× coverage. We also use the algorithm to assemble Escherichia coli with 30× coverage and the λ genome at a range of coverages from 3× to 50×. Additionally, we classify sequence variants at an order of magnitude lower coverage than is possible with existing methods.

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Figure 1: Nanopore sequencing fundamentals.
Figure 2: PoreSeq algorithm.
Figure 3: PoreSeq performance.

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European Nucleotide Archive

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Acknowledgements

We would like to thank E. Brandin for molecule preparation, D. Branton for obtaining MinION sequencers, S. Fleming for helpful algorithmic discussions and Figure 1a, and A. Kuan and M. Burns for feedback on this manuscript. The computations in this paper were run on the Odyssey cluster supported by the Faculty of Arts and Sciences Division of Science, Research Computing Group at Harvard University, and the work was supported by the National Institutes of Health Award no. R01HG003703 to J.A. Golovchenko and D. Branton.

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Authors and Affiliations

  1. School of Engineering and Applied Sciences, Harvard University, Cambridge, Massachusetts, USA

    Tamas Szalay & Jene A Golovchenko

  2. Department of Physics, Harvard University, Cambridge, Massachusetts, USA

    Jene A Golovchenko

Authors
  1. Tamas Szalay
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  2. Jene A Golovchenko
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Contributions

T.S.: algorithm development, data analysis and interpretation, writing of manuscript; J.A.G.: data analysis and interpretation, writing of manuscript.

Corresponding author

Correspondence to Jene A Golovchenko.

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The authors declare no competing financial interests.

Integrated supplementary information

Supplementary Figure 1 Allowed 5-mer transitions

An illustration of all of the 5-mer transitions from a single state (GCTAT), including normal steps (black), skips (red) and stays (green).

Supplementary Figure 2 Local likelihood alignment and mutation finding

a) The local (differential) log-likelihood from the candidate and an alternate strand, showing the high degree of noise in the data. b) The cumulative sum approach applied to the above data highlights the regions with beneficial mutations, shown with green shading.

Supplementary Figure 3 Optimizations used in PoreSeq

a) Matrix banding optimization shown, where the cells are only calculated in the blue band near the previous or estimated alignment (black line), while the rest of the matrix is implicitly set to 0 (white). b) Forward-backward optimization, where the matrix is calculated in both directions so that the full alignment score can be calculated from a single column in both matrices. In order to test a mutation in the orange region, only that handful of columns need to be recalculated in the forward direction.

Supplementary Figure 4 Flowcell runs used in this work

Details about all MinION flowcell runs used in the manuscript. Note in particular the larger spread in error for the λ DNA run as a result of an older sequencing kit (SQK-MAP003) being used, as well as the wider distribution of lengths due to the g-Tube shearing protocol. The λ run had 6831 reads total, of which 761 had 2D sequences and 700 aligned to the reference for a total coverage of around 125X. The M13 and CS runs had pass/fail filtering that selected for only 2D reads; M13 had 1195 reads total with 1113 aligned, for a depth of 1086, while the CS run had 907 reads with 860 aligned for an average depth of 720. While we recognize that other MAP participants have seen better performance and higher yield from certain flowcell runs, we found that the number of reads were sufficient for our purposes, and many of the issues we encountered (bubbles in the flowcells) have since been fixed by the manufacturer.

Supplementary Figure 5 Error analysis results for M13mp18

a) All errors are shown (labeled as % of total), binned by the base in the de novo sequence from all trials at 50× coverage against the true base in M13. b) The fraction of all deletions of a particular base that are part of a homopolymer region, defined as 5 or more identical contiguous bases (top), and the total number of each base belonging to homopolymer regions in M13 (bottom). Note that the distribution of homopolymer-related errors in (a) and (b) is largely a result of the underlying base-specific prevalence of homopolymer regions.

Supplementary information

Supplementary Text and Figures

Supplementary Notes 1–6; Supplementary Figures 1–5 (PDF 4266 kb)

Supplementary Data 1 (XLS 30 kb)

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Szalay, T., Golovchenko, J. De novo sequencing and variant calling with nanopores using PoreSeq. Nat Biotechnol 33, 1087–1091 (2015). https://doi.org/10.1038/nbt.3360

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