The DOI matches its Crossref record; 7 of the 7 references it cites were found in a registry or an index.
Every base matters: assessing small subunit rRNA primers for marine microbiomes with mock communities, time series and global field samples
Abstract
Summary: Microbial community analysis via high‐throughput sequencing of amplified 16S rRNA genes is an essential microbiology tool. We found the popular primer pair 515F ( 515F‐C ) and 806R greatly underestimated (e.g. SAR 11) or overestimated (e.g. Gammaproteobacteria) common marine taxa. We evaluated marine samples and mock communities (containing 11 or 27 marine 16S clones), showing alternative primers 515F‐ Y (5′‐ GTG Y CAGCMGCCGCGGTAA ) and 926R (5′‐ CCGYCAATTYMTTTRAGTTT ) yield more accurate estimates of mock community abundances, produce longer amplicons that can differentiate taxa unresolvable with 515F‐C / 806R , and amplify eukaryotic 18S rRNA . Mock communities amplified with 515F‐Y / 926R yielded closer observed community composition versus expected (r 2 = 0.95) compared with 515F‐Y / 806R (r 2 ∼ 0.5). Unexpectedly, biases with 515F‐Y / 806R against SAR11 in field samples (∼4–10‐fold) were stronger than in mock communities (∼2‐fold). Correcting a mismatch to T haumarchaea in the 515F‐C increased their apparent abundance in field samples, but not as much as using 926R rather than 806R . With plankton samples rich in eukaryotic DNA (> 1 μm size fraction), 18S sequences averaged ∼17% of all sequences. A single mismatch can strongly bias amplification, but even perfectly matched primers can exhibit preferential amplification. We show that beyond in silico predictions, testing with mock communities and field samples is important in primer selection.
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QIIME allows analysis of high-throughput community sequencing data
Caporaso, J. G., Kuczynski, J., Stombaugh, J., Bittinger, K., Bushman, F. D., Costello, E. K., Fierer, N., Peña, A. G., Goodrich, J. K., Gordon, J. I., et al. (2010) · Nature Methods · doi:10.1038/nmeth.f.303 · Cited by 34,887
The SILVA ribosomal RNA gene database project: improved data processing and web-based tools
Quast, C., Pruesse, E., Yilmaz, P., Gerken, J., Schweer, T., Yarza, P., Peplies, J., & Glöckner, F. O. (2012) · Nucleic Acids Research · doi:10.1093/nar/gks1219 · Cited by 33,055
Predictive functional profiling of microbial communities using 16S rRNA marker gene sequences
Langille, M. G. I., Zaneveld, J., Caporaso, J. G., McDonald, D., Knights, D., Reyes, J. A., Clemente, J. C., Burkepile, D. E., Vega Thurber, R. L., Knight, R., et al. (2013) · Nature Biotechnology · doi:10.1038/nbt.2676 · Cited by 9,116
Klindworth, A., Pruesse, E., Schweer, T., Peplies, J., Quast, C., Horn, M., & Glöckner, F. O. (2013) · Nucleic Acids Research · doi:10.1093/nar/gks808 · Cited by 8,850
Environmental genome shotgun sequencing of the sargasso sea
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SAR11 clade dominates ocean surface bacterioplankton communities
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A communal catalogue reveals Earth's multiscale microbial diversity
Thompson, L. R., Sanders, J. G., McDonald, D., Amir, A., Ladau, J., Locey, K. J., Prill, R. J., Tripathi, A., Gibbons, S. M., Ackermann, G., et al. (2017) · Nature · doi:10.1038/nature24621 · Cited by 2,684
Self-citationThe Madness of Microbiome: Attempting To Find Consensus “Best Practice” for 16S Microbiome Studies
Pollock, J., Glendinning, L., Wisedchanwet, T., & Watson, M. (2018) · Applied and Environmental Microbiology · doi:10.1128/aem.02627-17 · Cited by 575
Microbial abundance, activity and population genomic profiling with mOTUs2
Milanese, A., Mende, D. R., Paoli, L., Salazar, G., Ruscheweyh, H.-J., Cuenca, M., Hingamp, P., Alves, R., Costea, P. I., Coelho, L. P., et al. (2019) · Nature Communications · doi:10.1038/s41467-019-08844-4 · Cited by 446
Global diversity of microbial communities in marine sediment
Hoshino, T., Doi, H., Uramoto, G.-I., Wörmer, L., Adhikari, R. R., Xiao, N., Morono, Y., D’Hondt, S., Hinrichs, K.-U., & Inagaki, F. (2020) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1919139117 · Cited by 412
Understanding microbial community dynamics to improve optimal microbiome selection
Wright, R. J., Gibson, M. I., & Christie-Oleza, J. A. (2019) · Microbiome · doi:10.1186/s40168-019-0702-x · Cited by 341
Metadata
- Authors
- Parada, A. E., Needham, D. M., & Fuhrman, J. A.
- Venue
- Environmental Microbiology
- Publisher
- Wiley
- Published
- 2015-10-14
- Volume
- 18
- Issue
- 5
- Pages
- 1403-1414
- ISSN
- 1462-2912 (ISSN-L)
- 1462-2920
- OpenAlex
- W1952026831
- Cited by
- 4,007
- References
- 7
- FWCI
- 59.45
- Type
- journal article
- Language
- en
- Open access
- closed
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