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High-accuracy long-read amplicon sequences using unique molecular identifiers with Nanopore or PacBio sequencing

Nature Methods volume 18, pages 165–169 (2021)Cite this article

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Abstract

High-throughput amplicon sequencing of large genomic regions remains challenging for short-read technologies. Here, we report a high-throughput amplicon sequencing approach combining unique molecular identifiers (UMIs) with Oxford Nanopore Technologies (ONT) or Pacific Biosciences circular consensus sequencing, yielding high-accuracy single-molecule consensus sequences of large genomic regions. We applied our approach to sequence ribosomal RNA operon amplicons (~4,500 bp) and genomic sequences (>10,000 bp) of reference microbial communities in which we observed a chimera rate <0.02%. To reach a mean UMI consensus error rate <0.01%, a UMI read coverage of 15× (ONT R10.3), 25× (ONT R9.4.1) and 3× (Pacific Biosciences circular consensus sequencing) is needed, which provides a mean error rate of 0.0042%, 0.0041% and 0.0007%, respectively.

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Fig. 1: Dual UMI-tagging approach for long-read amplicon sequencing.
Fig. 2: Error profiling of long-read amplicon sequencing strategies.
Fig. 3: BLAST-based consensus taxonomic assignment against the Web of Life database for whole rRNA operons, using the combination of 16S and 23S rRNAs and individual rRNA genes.

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Data availability

Raw and assembled sequencing data are available at the European Nucleotide Archive under the project number PRJEB32674. A complete data overview is in Supplementary Table 11 and data yield is in Supplementary Table 12.

Public data used in this study include SILVA 138.1 SSURef Nr99 database (https://www.arb-silva.de/), gene-specific databases from the Web of Life (https://github.com/biocore/wol), Greengenes 13_5 database (https://greengenes.secondgenome.com/), EMP 16S V4 Deblur sOTU profiles (https://github.com/biocore/emp and https://github.com/biocore/redbiom), reference sequences for ZymoBIOMICS Microbial Community Standard D6300 (https://s3.amazonaws.com/zymo-files/BioPool/ZymoBIOMICS.STD.refseq.v2.zip), raw ONT sequencing data from ZymoBIOMICS Microbial Community Standard D6300 (ENA accession ERR2887847), Illumina sequencing data from ZymoBIOMICS Microbial Community Standard D6300 (ENA accessions: ERR2935851, ERR2935850, ERR2935852, ERR2935857, ERR2935854, ERR2935853, ERR2935848 and ERR2935849) and E. coli str_K12_MG1655 genome (NCBI: U00096.3).

Code availability

Source code and analysis scripts are freely available at https://github.com/SorenKarst/longread_umi. The repository release version used to generate the data in this article was v.0.4.2.

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Acknowledgements

The study was funded by research grants from VILLUM FONDEN (15510) and the Poul Due Jensen Foundation (Microflora Danica). R.M.Z. was funded by grants from the Natural Sciences and Engineering Research Council of Canada (Discovery Grant) and Genome British Columbia (SIP011).

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Author notes

  1. These authors contributed equally: Søren M. Karst, Ryan M. Ziels.

Authors and Affiliations

  1. Center for Microbial Communities, Department of Chemistry and Bioscience, Aalborg University, Aalborg, Denmark

    Søren M. Karst, Rasmus H. Kirkegaard, Emil A. Sørensen & Mads Albertsen

  2. Department of Civil Engineering, The University of British Columbia, Vancouver, British Columbia, Canada

    Ryan M. Ziels

  3. Department of Pediatrics, University of California San Diego, La Jolla, CA, USA

    Daniel McDonald, Qiyun Zhu & Rob Knight

  4. Department of Bioengineering, University of California San Diego, La Jolla, CA, USA

    Rob Knight

  5. Center for Microbiome Innovation, University of California San Diego, La Jolla, CA, USA

    Rob Knight

  6. Department of Computer Science and Engineering, University of California San Diego, La Jolla, CA, USA

    Rob Knight

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Contributions

S.M.K. and R.M.Z. conceived the method and developed the bioinformatics pipeline. S.M.K. performed the wet laboratory method development and experiments. E.A.S. performed Nanopore UMI sequencing of E. coli. R.H.K. assembled reference genomes. S.M.K., R.M.Z. and M.A. performed data analysis on method performance. D.M., Q.Z. and R.K. analyzed American Gut Project samples. S.M.K., R.M.Z. and M.A. wrote the first draft of the manuscript. All authors contributed to the content and revision of the manuscript.

Corresponding author

Correspondence to Mads Albertsen.

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Competing interests

M.A., S.M.K. and R.H.K. are co-owners of DNASense ApS. The other authors declare no competing interests.

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Peer review information Lei Tang was the primary editor on this article and managed its editorial process and peer review in collaboration with the rest of the editorial team.

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Human research participants The American Gut Project relies primarily on crowd-sourced samples without active recruitment. This research was performed in accordance with the University of Colorado Boulder’s Institutional Review Board protocol number 12-0582 and the University of California San Diego’s Human Research Protection Program, protocol no. 141853.

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Supplementary Note, Figs. 1–18, Tables 1–13 and references.

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Karst, S.M., Ziels, R.M., Kirkegaard, R.H. et al. High-accuracy long-read amplicon sequences using unique molecular identifiers with Nanopore or PacBio sequencing. Nat Methods 18, 165–169 (2021). https://doi.org/10.1038/s41592-020-01041-y

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