Retrieval of a million high-quality, full-length microbial 16S and 18S rRNA gene sequences without primer bias

Abstract

Small subunit ribosomal RNA (SSU rRNA) genes, 16S in bacteria and 18S in eukaryotes, have been the standard phylogenetic markers used to characterize microbial diversity and evolution for decades. However, the reference databases of full-length SSU rRNA gene sequences are skewed to well-studied ecosystems and subject to primer bias and chimerism, which results in an incomplete view of the diversity present in a sample. We combine poly(A)-tailing and reverse transcription of SSU rRNA molecules with synthetic long-read sequencing to generate high-quality, full-length SSU rRNA sequences, without primer bias, at high throughput. We apply our approach to samples from seven different ecosystems and obtain more than a million SSU rRNA sequences from all domains of life, with an estimated raw error rate of 0.17%. We observe a large proportion of novel diversity, including several deeply branching phylum-level lineages putatively related to the Asgard Archaea. Our approach will enable expansion of the SSU rRNA reference databases by orders of magnitude, and contribute to a comprehensive census of the tree of life.

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Figure 1: Full-length SSU rRNA sequencing.
Figure 2: Coverage of the tree of life.
Figure 3: Coverage of the domain Archaea.

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European Nucleotide Archive

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Acknowledgements

The study was funded by the Danish Research Council for Independent Research (FTP, grant 6111-00617B), the Innovation Fund Denmark (1305-00018B, NomiGas), the Villum Foundation (grant VKR 022796 and 13351), and the Poul Due Jensen (Grundfos) Foundation. S.J.M. was supported by a Danish Council for Independent Research grant (no. 4093-00127A). M.A. was supported by a research grant (15510) from VILLUM FONDEN. We thank H. Daims and M. Wagner for insightful discussions of the manuscript.

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Authors

Contributions

S.M.K., M.S.D. and M.A. conceived the method. S.M.K. and M.S.D. performed wet lab method development and experiments. R.H.K. performed Nanopore sequencing and data analysis. S.M.K. and M.A. developed the bioinformatics pipeline and performed data analysis. S.J.M. performed the phylogenetic analysis. S.M.K., M.S.D., S.J.M., R.H.K., P.H.N. and M.A. wrote the manuscript.

Corresponding author

Correspondence to Mads Albertsen.

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Competing interests

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

Integrated supplementary information

Supplementary Figure 1 Detailed overview of the primer-free full-length SSU rRNA library preparation.

Detailed overview of the primer-free full-length SSU rRNA library preparation.

Supplementary Figure 2 Detailed overview of the primer-based full-length SSU rRNA library preparation.

Detailed overview of the primer-based full-length SSU rRNA library preparation.

Supplementary Figure 3 rRNA length distribution after adapter trimming.

rRNA length distribution after adapter trimming.

Supplementary Figure 4 Maximum-likelihood phylogenetic tree showing coverage of the domain Bacteria.

Maximum-likelihood phylogenetic tree showing coverage of the domain Bacteria. The tree includes all bacterial OTUs clustered at 97% generated in this study, their closest match in the Silva SSU NR99 v. 128 database and the reference set from the recent Tree of Life article (Hug et al., 2016). Hypervariable regions were masked with a 40% positional conservation filter, giving 1392 alignment positions, and the tree calculated using FastTree v. 2.1.3 SSE3 (Price et al., 2010). Clade names and clustering are based on the position of reference sequences. *Indicates clades that do not include a genome or pure culture reference sequence – being based on classification of reference sequences in the Silva v. 128 taxonomy. Reference sequences appear black whilst those generated in the current study are color coded based on their similarity to existing database sequences.

Supplementary Figure 5 Rarefaction curves for the different samples split based on kingdom.

Rarefaction curves for the different samples split based on kingdom.

Supplementary Figure 6 Maximum-likelihood phylogenetic tree showing coverage of the domain Archaea.

Maximum-likelihood phylogenetic tree showing coverage of the domain Archaea. The tree includes all archaeal OTUs clustered at 97% generated in this study, their closest match in the Silva SSU NR99 v. 128 database and the reference set from the recent Tree of Life article (Hug et al., 2016). Hypervariable regions were masked with a 40% positional conservation filter, giving 1257 alignment positions, and the tree calculated using FastTree v. 2.1.3 S SE3 (Price et al., 2010). Clade names and clustering are based on the position of reference sequences. *Indicates clades that do not include a genome or pure culture reference sequence – being based on classification of reference sequences in the Silva v. 1.28 taxonomy. Reference sequences appear black whilst those generated in the current study are color coded based on their similarity to existing database sequences.

Supplementary Figure 7 Maximum-likelihood phylogenetic tree showing coverage of the domain Eukarya.

Maximum-likelihood phylogenetic tree showing coverage of the domain Eukarya. The tree includes all eukaryotic OTUs clustered at 97% generated in this study, their closest match in the Silva SSU NR99 v. 128 database and the reference set from the recent Tree of Life article (Hug et al., 2016). Hypervariable regions were masked with a 40% positional conservation filter, giving 1548 alignment positions, and the tree calculated using FastTree v. 2.1.3 S SE3 (Price et al., 2010). Clade names and clustering are based on the position of reference sequences. Reference sequences appear black whilst those generated in the current study are color coded based on their similarity to existing database sequences.

Supplementary Figure 8 Error-correction of Oxford Nanopore MinION data using molecular tagging.

Error-correction of Oxford Nanopore MinION data using molecular tagging. Error-rate of the individual error-corrected consensus sequences as a function of the number of reads used to generate the consensus sequence. “1” represents the raw 2D reads.

Supplementary Figure 9 Data processing overview.

Data processing overview. An overview of the data processing steps, important data outputs and data types used for different analysis.

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Karst, S., Dueholm, M., McIlroy, S. et al. Retrieval of a million high-quality, full-length microbial 16S and 18S rRNA gene sequences without primer bias. Nat Biotechnol 36, 190–195 (2018). https://doi.org/10.1038/nbt.4045

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