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Accurate taxonomy assignments from 16S rRNA sequences produced by highly parallel pyrosequencers
Open accessAbstract
The recent introduction of massively parallel pyrosequencers allows rapid, inexpensive analysis of microbial community composition using 16S ribosomal RNA (rRNA) sequences. However, a major challenge is to design a workflow so that taxonomic information can be accurately and rapidly assigned to each read, so that the composition of each community can be linked back to likely ecological roles played by members of each species, genus, family or phylum. Here, we use three large 16S rRNA datasets to test whether taxonomic information based on the full-length sequences can be recaptured by short reads that simulate the pyrosequencer outputs. We find that different taxonomic assignment methods vary radically in their ability to recapture the taxonomic information in full-length 16S rRNA sequences: most methods are sensitive to the region of the 16S rRNA gene that is targeted for sequencing, but many combinations of methods and rRNA regions produce consistent and accurate results. To process large datasets of partial 16S rRNA sequences obtained from surveys of various microbial communities, including those from human body habitats, we recommend the use of Greengenes or RDP classifier with fragments of at least 250 bases, starting from one of the primers R357, R534, R798, F343 or F517.
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Basic local alignment search tool
Altschul, S. F., Gish, W., Miller, W., Myers, E. W., & Lipman, D. J. (1990) · Journal of Molecular Biology · doi:10.1006/jmbi.1990.9999 · Cited by 98,669
An obesity-associated gut microbiome with increased capacity for energy harvest
Turnbaugh, P. J., Ley, R. E., Mahowald, M. A., Magrini, V., Mardis, E. R., & Gordon, J. I. (2006) · Nature · doi:10.1038/nature05414 · Cited by 12,444
Greengenes, a Chimera-Checked 16S rRNA Gene Database and Workbench Compatible with ARB
DeSantis, T. Z., Hugenholtz, P., Larsen, N., Rojas, M., Brodie, E. L., Keller, K., Huber, T., Dalevi, D., Hu, P., & Andersen, G. L. (2006) · Applied and Environmental Microbiology · doi:10.1128/aem.03006-05 · Cited by 10,891
Self-citationHuman gut microbes associated with obesity
Ley, R. E., Turnbaugh, P. J., Klein, S., & Gordon, J. I. (2006) · Nature · doi:10.1038/4441022a · Cited by 8,785
UniFrac: a New Phylogenetic Method for Comparing Microbial Communities
Lozupone, C., & Knight, R. (2005) · Applied and Environmental Microbiology · doi:10.1128/aem.71.12.8228-8235.2005 · Cited by 8,227
Self-citationObesity alters gut microbial ecology
Ley, R. E., Bäckhed, F., Turnbaugh, P., Lozupone, C. A., Knight, R. D., & Gordon, J. I. (2005) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0504978102 · Cited by 6,176
Self-citation
Research works printed but not linked: 24
- 2005, 437, 376, 10.1038/nature03959 · DOI · Paper page
- Rapp, 2003, Annual review of microbiology, 57, 369, 10.1146/annurev.micro.57.030502.090759 · DOI · Paper page
- Pace, 1997, Science, 276, 734, 10.1126/science.276.5313.734 · DOI · Paper page
- 2007, PLOS ONE, 2, 197e, 10.1371/journal.pone.0000197 · DOI · Paper page
- 2008, 5, 235, 10.1038/nmeth.1184 · DOI · Paper page
- McKenna, 2008, 4, e20, 10.1371/journal.ppat.0040020 · DOI · Paper page
- 2007, Nucleic Acids Research, 35, e120, 10.1093/nar/gkm541 · DOI
- Lozupone, 2006, BMC bioinformatics [electronic resource], 7, 371, 10.1186/1471-2105-7-371 · DOI · Paper page
- 2007, Applied and Environmental Microbiology, 73, 1576, 10.1128/AEM.01996-06 · DOI · Paper page
- 2005, 1, e3, 10.1371/journal.pcbi.0010003 · DOI
- 2008, Nucleic Acids Research, 36, 2230, 10.1093/nar/gkn038 · DOI · Paper page
- 2007, Nucleic Acids Research, 35, D169, 10.1093/nar/gkl889 · DOI
- 2007, 449, 804, 10.1038/nature06244 · DOI · Paper page
- 2005, Science, 308, 1635, 10.1126/science.1110591 · DOI · Paper page
- Ley, 2006, Applied and Environmental Microbiology, 72, 3685, 10.1128/AEM.72.5.3685-3695.2006 · DOI
- DeSantis, 2006, Nucleic Acids Research, 34, W394, 10.1093/nar/gkl244 · DOI · Paper page
- Desantis, 2007, Microbial Ecology, 53, 371, 10.1007/s00248-006-9134-9 · DOI · Paper page
- 2006, Bioinformatics, 22, 2823, 10.1093/bioinformatics/btl478 · DOI
- Ludwig, 2004, Nucleic Acids Research, 32, 1363, 10.1093/nar/gkh293 · DOI
- Hao, 2004, Journal of bioinformatics and computational biology, 2, 1, 10.1142/S0219720004000442 · DOI
- McHardy, 2007, 4, 63, 10.1038/nmeth976 · DOI · Paper page
- 1970, Systematic Biology, 19, 83, 10.1093/sysbio/19.1.83 · DOI · Paper page
- Knight, 2007, Genome biology, 8, R171, 10.1186/gb-2007-8-8-r171 · DOI
- Huber, 2007, Science, 318, 97, 10.1126/science.1146689 · DOI · Paper page
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Jones, R. T., Robeson, M. S., Lauber, C. L., Hamady, M., Knight, R., & Fierer, N. (2009) · The ISME Journal · doi:10.1038/ismej.2008.127 · Cited by 1,230
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Iliev, I. D., Funari, V. A., Taylor, K. D., Nguyen, Q., Reyes, C. N., Strom, S. P., Brown, J., Becker, C. A., Fleshner, P. R., Dubinsky, M., et al. (2012) · Science · doi:10.1126/science.1221789 · Cited by 1,073
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Erb-Downward, J. R., Thompson, D. L., Han, M. K., Freeman, C. M., McCloskey, L., Schmidt, L. A., Young, V. B., Toews, G. B., Curtis, J. L., Sundaram, B., et al. (2011) · PLoS ONE · doi:10.1371/journal.pone.0016384 · Cited by 973
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Walters, W. A., Xu, Z., & Knight, R. (2014) · FEBS Letters · doi:10.1016/j.febslet.2014.09.039 · Cited by 836
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Metadata
- Authors
- Liu, Z., DeSantis, T. Z., Andersen, G. L., & Knight, R.
- Venue
- Nucleic Acids Research
- Publisher
- Oxford University Press (OUP)
- Published
- 2008-09-06
- Volume
- 36
- Issue
- 18
- Pages
- e120-e120
- ISSN
- 0305-1048 (ISSN-L)
- 1362-4962
- OpenAlex
- W2158795865
- Cited by
- 592
- References
- 6
- FWCI
- 13.52
- Type
- journal article
- Language
- en
- Open access
- gold
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