The DOI matches its Crossref record; 41 of the 41 references it cites were found in a registry or an index.
Analysis, Optimization and Verification of Illumina-Generated 16S rRNA Gene Amplicon Surveys
Open accessAbstract
The exploration of microbial communities by sequencing 16S rRNA genes has expanded with low-cost, high-throughput sequencing instruments. Illumina-based 16S rRNA gene sequencing has recently gained popularity over 454 pyrosequencing due to its lower costs, higher accuracy and greater throughput. Although recent reports suggest that Illumina and 454 pyrosequencing provide similar beta diversity measures, it remains to be demonstrated that pre-existing 454 pyrosequencing workflows can transfer directly from 454 to Illumina MiSeq sequencing by simply changing the sequencing adapters of the primers. In this study, we modified 454 pyrosequencing primers targeting the V4-V5 hyper-variable regions of the 16S rRNA gene to be compatible with Illumina sequencers. Microbial communities from cows, humans, leeches, mice, sewage, and termites and a mock community were analyzed by 454 and MiSeq sequencing of the V4-V5 region and MiSeq sequencing of the V4 region. Our analysis revealed that reference-based OTU clustering alone introduced biases compared to de novo clustering, preventing certain taxa from being observed in some samples. Based on this we devised and recommend an analysis pipeline that includes read merging, contaminant filtering, and reference-based clustering followed by de novo OTU clustering, which produces diversity measures consistent with de novo OTU clustering analysis. Low levels of dataset contamination with Illumina sequencing were discovered that could affect analyses that require highly sensitive approaches. While moving to Illumina-based sequencing platforms promises to provide deeper insights into the breadth and function of microbial diversity, our results show that care must be taken to ensure that sequencing and processing artifacts do not obscure true microbial diversity.
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Cites
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
UCHIME improves sensitivity and speed of chimera detection
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Greengenes, a Chimera-Checked 16S rRNA Gene Database and Workbench Compatible with ARB
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Global patterns of 16S rRNA diversity at a depth of millions of sequences per sample
Caporaso, J. G., Lauber, C. L., Walters, W. A., Berg-Lyons, D., Lozupone, C. A., Turnbaugh, P. J., Fierer, N., & Knight, R. (2011) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1000080107 · Cited by 9,711
Ultra-high-throughput microbial community analysis on the Illumina HiSeq and MiSeq platforms
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A core gut microbiome in obese and lean twins
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Cited by
Shotgun metagenomics, from sampling to analysis
Quince, C., Walker, A. W., Simpson, J. T., Loman, N. J., & Segata, N. (2017) · Nature Biotechnology · doi:10.1038/nbt.3935 · Cited by 2,202
FROGS: Find, Rapidly, OTUs with Galaxy Solution
Escudié, F., Auer, L., Bernard, M., Mariadassou, M., Cauquil, L., Vidal, K., Maman, S., Hernandez-Raquet, G., Combes, S., & Pascal, G. (2018) · Bioinformatics · doi:10.1093/bioinformatics/btx791 · Cited by 1,018
Systematic improvement of amplicon marker gene methods for increased accuracy in microbiome studies
Gohl, D. M., Vangay, P., Garbe, J., MacLean, A., Hauge, A., Becker, A., Gould, T. J., Clayton, J. B., Johnson, T. J., Hunter, R., et al. (2016) · Nature Biotechnology · doi:10.1038/nbt.3601 · Cited by 819
The Microbiome Quality Control Project Consortium, Sinha, R., Abu-Ali, G., Vogtmann, E., Fodor, A. A., Ren, B., Amir, A., Schwager, E., Crabtree, J., Ma, S., et al. (2017) · Nature Biotechnology · doi:10.1038/nbt.3981 · Cited by 587
D’Amore, R., Ijaz, U. Z., Schirmer, M., Kenny, J. G., Gregory, R., Darby, A. C., Shakya, M., Podar, M., Quince, C., & Hall, N. (2016) · BMC Genomics · doi:10.1186/s12864-015-2194-9 · Cited by 405
Metadata
- Authors
- Nelson, M. C., Morrison, H. G., Benjamino, J., Grim, S. L., & Graf, J.
- Venue
- PLoS ONE
- Publisher
- Public Library of Science (PLoS)
- Published
- 2014-04-10
- Volume
- 9
- Issue
- 4
- Pages
- e94249
- ISSN
- 1932-6203 (ISSN-L)
- OpenAlex
- W1986908904
- Cited by
- 324
- References
- 41
- FWCI
- 15.94
- Type
- journal article
- Language
- en
- Open access
- gold
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- CoChat. (2026). Citation check: Analysis, Optimization and Verification of Illumina-Generated 16S rRNA Gene Amplicon Surveys. https://app.cochat.ai/papers/10.1371/journal.pone.0094249
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- Nelson, M. C., Morrison, H. G., Benjamino, J., Grim, S. L., & Graf, J. (2014). Analysis, Optimization and Verification of Illumina-Generated 16S rRNA Gene Amplicon Surveys. PLoS ONE. https://doi.org/10.1371/journal.pone.0094249
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
